diff --git a/README.rst b/README.rst index 17f65f5b..f444b684 100644 --- a/README.rst +++ b/README.rst @@ -31,8 +31,8 @@ QSDsan: Quantitative Sustainable Design for Sanitation and Resource Recovery Sys :target: https://codecov.io/gh/QSD-Group/QSDsan .. Binder launch of tutorials -.. image:: https://mybinder.org/badge_logo.svg - :target: https://mybinder.org/v2/gh/QSD-Group/QSDsan/main?filepath=%2Fdocs%2Fsource%2Ftutorials +.. image:: ./docs/source/images/custom_binder_logo.svg + :target: https://mybinder.org/v2/gh/QSD-Group/QSDsan-env/main?urlpath=git-pull%3Frepo%3Dhttps%253A%252F%252Fgithub.com%252FQSD-group%252FQSDsan%26urlpath%3Dtree%252FQSDsan%252Fdocs%252Fsource%252Ftutorials%26branch%3Dmain .. Email subscription form .. image:: https://img.shields.io/badge/news-subscribe-F3A93C?style=flat&logo=rss @@ -162,4 +162,8 @@ References .. [2] Li, Y.; Trimmer, J.T.; Hand, S.; Zhang, X.; Chambers, K.G.; Lohman, H.A.C.; Shi, R.; Byrne, D.M.; Cook, S.M.; Guest, J.S. Quantitative Sustainable Design (QSD): A Methodology for the Prioritization of Research, Development, and Deployment of Technologies. (Tutorial Review) Environ. Sci.: Water Res. Technol. 2022, 8 (11), 2439–2465. https://doi.org/10.1039/D2EW00431C. -.. [3] Cortés-Peña, Y.; Kumar, D.; Singh, V.; Guest, J.S. BioSTEAM: A Fast and Flexible Platform for the Design, Simulation, and Techno-Economic Analysis of Biorefineries under Uncertainty. ACS Sustainable Chem. Eng. 2020, 8 (8), 3302–3310. https://doi.org/10.1021/acssuschemeng.9b07040. \ No newline at end of file +.. [3] Cortés-Peña, Y.; Kumar, D.; Singh, V.; Guest, J.S. BioSTEAM: A Fast and Flexible Platform for the Design, Simulation, and Techno-Economic Analysis of Biorefineries under Uncertainty. ACS Sustainable Chem. Eng. 2020, 8 (8), 3302–3310. https://doi.org/10.1021/acssuschemeng.9b07040. + + +.. Custom launch badges: https://mybinder.readthedocs.io/en/latest/howto/badges.html +.. binder_badge: https://img.shields.io/badge/launch-binder%20%7C%20tutorial-579ACA.svg?logo=data:image/png;base64,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 diff --git a/docs/source/api/sanunits/_index.rst b/docs/source/api/sanunits/_index.rst index 1c1eab87..72aa4069 100644 --- a/docs/source/api/sanunits/_index.rst +++ b/docs/source/api/sanunits/_index.rst @@ -37,7 +37,6 @@ Individual Unit Operations CropApplication DynamicInfluent ElectrochemicalCell - encapsulation_bioreactor Excretion Flash heat_exchanging diff --git a/docs/source/api/sanunits/encapsulation_bioreactor.rst b/docs/source/api/sanunits/encapsulation_bioreactor.rst deleted file mode 100644 index bc4d9476..00000000 --- a/docs/source/api/sanunits/encapsulation_bioreactor.rst +++ /dev/null @@ -1,4 +0,0 @@ -Encapsulation Bioreactor -======================== -.. automodule:: qsdsan.sanunits._encapsulation_bioreactor - :members: \ No newline at end of file diff --git a/docs/source/conf.py b/docs/source/conf.py index 2e7bbf8b..524df415 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -31,7 +31,7 @@ # built documents. # # The short X.Y version. -version = '1.3.0' +version = '1.3.1' # The full version, including alpha/beta/rc tags. release = version @@ -127,10 +127,10 @@ # -- External mapping ------------------------------------------------------- intersphinx_mapping = { - 'biosteam': ('https://biosteam.readthedocs.io/en/latest', None), - 'thermosteam': ('https://biosteam.readthedocs.io/en/latest', None), - 'BioSTEAM': ('https://biosteam.readthedocs.io/en/latest', None), - 'Thermosteam': ('https://biosteam.readthedocs.io/en/latest', None), - 'scipy': ('https://docs.scipy.org/doc/scipy/', None), - 'SALib': ('https://salib.readthedocs.io/en/latest', None), + 'biosteam': ('https://biosteam.readthedocs.io', None), + 'thermosteam': ('https://biosteam.readthedocs.io', None), + 'BioSTEAM': ('https://biosteam.readthedocs.io', None), + 'Thermosteam': ('https://biosteam.readthedocs.io', None), + 'scipy': ('https://docs.scipy.org/doc/scipy', None), + 'SALib': ('https://salib.readthedocs.io', None), } diff --git a/docs/source/images/custom_binder_logo.svg b/docs/source/images/custom_binder_logo.svg new file mode 100644 index 00000000..00919718 --- /dev/null +++ b/docs/source/images/custom_binder_logo.svg @@ -0,0 +1 @@ +launch: binder | tutoriallaunchbinder | tutorial \ No newline at end of file diff --git a/docs/source/tutorials/0_Quick_Overview.ipynb b/docs/source/tutorials/0_Quick_Overview.ipynb index ed9e66f4..5f4341ec 100644 --- a/docs/source/tutorials/0_Quick_Overview.ipynb +++ b/docs/source/tutorials/0_Quick_Overview.ipynb @@ -9,9 +9,9 @@ "\n", "- **Prepared by:**\n", " \n", - " - [Yalin Li](https://qsdsan.readthedocs.io/en/latest/authors/Yalin_Li.html)\n", + " - [Yalin Li](https://qsdsan.readthedocs.io/en/latest/CONTRIBUTING.html)\n", " \n", - "To run tutorials in your browser, go to this [Binder page](https://mybinder.org/v2/gh/QSD-Group/QSDsan/main?filepath=%2Fdocs%2Fsource%2Ftutorials)." + "To run tutorials in your browser, go to this [Binder page](https://mybinder.org/v2/gh/QSD-Group/QSDsan-env/main?urlpath=git-pull%3Frepo%3Dhttps%253A%252F%252Fgithub.com%252FQSD-group%252FQSDsan%26urlpath%3Dtree%252FQSDsan%252Fdocs%252Fsource%252Ftutorials%26branch%3Dmain)." ] }, { @@ -605,7 +605,36 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.9.13" + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false } }, "nbformat": 4, diff --git a/docs/source/tutorials/10_Process.ipynb b/docs/source/tutorials/10_Process.ipynb index 7cc73a09..5d1069ff 100644 --- a/docs/source/tutorials/10_Process.ipynb +++ b/docs/source/tutorials/10_Process.ipynb @@ -9,7 +9,7 @@ "\n", "- **Prepared by:**\n", " \n", - " - [Joy Zhang](https://qsdsan.readthedocs.io/en/latest/authors/Joy_Zhang.html)\n", + " - [Joy Zhang](https://qsdsan.readthedocs.io/en/latest/CONTRIBUTING.html)\n", " \n", "- **Covered topics:**\n", "\n", @@ -20,9 +20,9 @@ " \n", "- **Video demo:**\n", "\n", - " - [Joy Zhang](https://qsdsan.readthedocs.io/en/latest/authors/Joy_Zhang.html)\n", + " - [Joy Zhang](https://qsdsan.readthedocs.io/en/latest/CONTRIBUTING.html)\n", " \n", - "To run tutorials in your browser, go to this [Binder page](https://mybinder.org/v2/gh/QSD-Group/QSDsan/main?filepath=%2Fdocs%2Fsource%2Ftutorials).\n", + "To run tutorials in your browser, go to this [Binder page](https://mybinder.org/v2/gh/QSD-Group/QSDsan-env/main?urlpath=git-pull%3Frepo%3Dhttps%253A%252F%252Fgithub.com%252FQSD-group%252FQSDsan%26urlpath%3Dtree%252FQSDsan%252Fdocs%252Fsource%252Ftutorials%26branch%3Dmain).\n", " \n", "You can also watch a video demo on YouTube ([part 1](https://youtu.be/r9HrfTH9_Tg), [part 2](https://youtu.be/noVSJboqSuc)) (subscriptions & likes appreciated!)." ] @@ -82,15 +82,7 @@ "execution_count": 2, "id": "3dc1138e", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "This tutorial was made with qsdsan v1.2.5.\n" - ] - } - ], + "outputs": [], "source": [ "import qsdsan as qs\n", "print(f'This tutorial was made with qsdsan v{qs.__version__}.')" @@ -117,24 +109,10 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "id": "61b1bd62", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "Image(url='https://lucid.app/publicSegments/view/2c231fa2-6065-46b9-83af-a790ce38b6c0/image.png', width=600)" ] @@ -158,21 +136,10 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "id": "75766cf7", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "qsdsan._process.Process" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# If you check\n", "qs.Process\n", @@ -192,21 +159,10 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "id": "50db4564", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# If you check\n", "qs.processes" @@ -222,37 +178,10 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "id": "fc717b78", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "('DiffusedAeration',\n", - " 'create_asm1_cmps',\n", - " 'ASM1',\n", - " 'create_asm2d_cmps',\n", - " 'ASM2d',\n", - " 'create_adm1_cmps',\n", - " 'ADM1',\n", - " 'non_compet_inhibit',\n", - " 'substr_inhibit',\n", - " 'T_correction_factor',\n", - " 'pH_inhibit',\n", - " 'Hill_inhibit',\n", - " 'rhos_adm1',\n", - " 'Decay',\n", - " 'KineticReaction',\n", - " 'create_pm2_cmps',\n", - " 'PM2')" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# To see the list of objects that can be directly imported from this folder\n", "qs.processes.__all__" @@ -270,39 +199,12 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "id": "6e6931bb", "metadata": { "scrolled": true }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'__all__',\n", - " '__builtins__',\n", - " '__cached__',\n", - " '__doc__',\n", - " '__file__',\n", - " '__loader__',\n", - " '__name__',\n", - " '__package__',\n", - " '__path__',\n", - " '__spec__',\n", - " '_adm1',\n", - " '_aeration',\n", - " '_asm1',\n", - " '_asm2d',\n", - " '_decay',\n", - " '_kinetic_reaction',\n", - " '_pm2'}" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# You can see other attributes of the `qs.processes` folder with the `dir` function\n", "set(dir(qs.processes)) - set(qs.processes.__all__)" @@ -318,21 +220,10 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "id": "82751cf8", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# For example, `_asm1.py` is a script in the `processes` folder.\n", "qs.processes._asm1" @@ -363,18 +254,10 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "id": "7e4b98a4", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CompiledComponents([S_I, S_S, X_I, X_S, X_BH, X_BA, X_P, S_O, S_NO, S_NH, S_ND, X_ND, S_ALK, S_N2, H2O])\n" - ] - } - ], + "outputs": [], "source": [ "# Before we get to the subsections, let's get ready by loading ASM1-related objects in qsdsan\n", "from qsdsan.processes import create_asm1_cmps, ASM1\n", @@ -394,27 +277,10 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "id": "2b7ccd5b", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Thermo(\n", - " chemicals=CompiledComponents([S_I, S_S, X_I, X_S, X_BH, X_BA, X_P, S_O, S_NO, S_NH, S_ND, X_ND, S_ALK, S_N2, H2O]),\n", - " mixture=Mixture(\n", - " rule='ideal', ...\n", - " include_excess_energies=False\n", - " ),\n", - " Gamma=DortmundActivityCoefficients,\n", - " Phi=IdealFugacityCoefficients,\n", - " PCF=MockPoyintingCorrectionFactors\n", - ")\n" - ] - } - ], + "outputs": [], "source": [ "# By default, the thermo is set with this `CompiledComponents` object upon its creation.\n", "# We can verify that by calling the `get_thermo` function\n", @@ -423,21 +289,10 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "id": "948340c9", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(qsdsan._process.CompiledProcesses,)" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Next we need to create an instance of the ASM1 model\n", "# We can see that `ASM1` is a subclass of `CompiledProcesses`, so it can be used for demonstration\n", @@ -447,7 +302,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "id": "7357bed8", "metadata": { "scrolled": true @@ -460,20 +315,12 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "id": "bdf27943", "metadata": { "scrolled": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ASM1([aero_growth_hetero, anox_growth_hetero, aero_growth_auto, decay_hetero, decay_auto, ammonification, hydrolysis, hydrolysis_N])\n" - ] - } - ], + "outputs": [], "source": [ "# Without getting into the details of ASM1, we will leave all parameters at their default values \n", "asm1 = ASM1()\n", @@ -503,43 +350,10 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "id": "25a826a8", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Process: aero_growth_hetero\n", - "[stoichiometry] S_S: -1.0/Y_H\n", - " X_BH: 1.00\n", - " S_O: 1.0*(Y_H - 1.0)/Y_H\n", - " S_NH: -0.0800\n", - " S_ALK: -0.0686\n", - "[reference] X_BH\n", - "[rate equation] S_NH*S_O*S_S*X_BH*mu_H/((K_N...\n", - "[parameters] Y_H: 0.67\n", - " Y_A: 0.24\n", - " f_P: 0.08\n", - " mu_H: 4\n", - " K_S: 10\n", - " K_O_H: 0.2\n", - " K_NO: 0.5\n", - " b_H: 0.3\n", - " mu_A: 0.5\n", - " K_NH: 1\n", - " K_O_A: 0.4\n", - " b_A: 0.05\n", - " eta_g: 0.8\n", - " k_a: 0.05\n", - " k_h: 3\n", - " K_X: 0.1\n", - " eta_h: 0.8\n", - "[dynamic parameters] \n" - ] - } - ], + "outputs": [], "source": [ "# Let's take the 0th process in `asm1` as an example to learn about `Process`:\n", "# p1 = asm1.tuple[0]\n", @@ -573,25 +387,10 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "id": "baea578a", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'S_S': -1.49253731343284,\n", - " 'X_BH': 1.00000000000000,\n", - " 'S_O': -0.492537313432836,\n", - " 'S_NH': -0.0800000000000000,\n", - " 'S_ALK': -0.0685997415522571}" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# For example, we can retrieve information on the stoichiometry of this process\n", "p1.stoichiometry" @@ -611,21 +410,10 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "id": "acb5b8e0", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'X_BH'" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# This information can also be accessed by calling the `ref_component` property.\n", "p1.ref_component" @@ -633,26 +421,12 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "id": "d0612896", "metadata": { "scrolled": true }, - "outputs": [ - { - "data": { - "text/latex": [ - "$\\displaystyle \\frac{4.0 S_{NH} S_{O} S_{S} X_{BH}}{\\left(S_{NH} + 1.0\\right) \\left(S_{O} + 0.2\\right) \\left(S_{S} + 10.0\\right)}$" - ], - "text/plain": [ - "4.0*S_NH*S_O*S_S*X_BH/((S_NH + 1.0)*(S_O + 0.2)*(S_S + 10.0))" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Another defining characteristics of a process is its rate equation, which is stored as a\n", "# property of the `Process` object\n", @@ -671,24 +445,10 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "id": "0ce7b4df", "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "$\\displaystyle \\frac{S_{NH} S_{O} S_{S} X_{BH} \\mu_{H}}{\\left(K_{NH} + S_{NH}\\right) \\left(K_{O H} + S_{O}\\right) \\left(K_{S} + S_{S}\\right)}$" - ], - "text/plain": [ - "S_NH*S_O*S_S*X_BH*mu_H/((K_NH + S_NH)*(K_O_H + S_O)*(K_S + S_S))" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# If we access the private attribute\n", "p1._rate_equation" @@ -704,7 +464,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "id": "50b77ac8", "metadata": {}, "outputs": [], @@ -722,21 +482,10 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 20, "id": "0142f901", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Now that we've understood the required input to the rate equation, we can try evaluating the\n", "# process rate. Let's try with all component concentrations equal to 1.\n", @@ -755,21 +504,10 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 21, "id": "aceafb27", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.15151515151515152" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Note that the `rate_equation` attribute only stores the formula.\n", "# The evaluation of process rate is done through the `rate_function` attribute, which is rendered\n", @@ -791,37 +529,10 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 22, "id": "62dc9537", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'Y_H': 0.67,\n", - " 'Y_A': 0.24,\n", - " 'f_P': 0.08,\n", - " 'mu_H': 4.0,\n", - " 'K_S': 10.0,\n", - " 'K_O_H': 0.2,\n", - " 'K_NO': 0.5,\n", - " 'b_H': 0.3,\n", - " 'mu_A': 0.5,\n", - " 'K_NH': 1.0,\n", - " 'K_O_A': 0.4,\n", - " 'b_A': 0.05,\n", - " 'eta_g': 0.8,\n", - " 'k_a': 0.05,\n", - " 'k_h': 3.0,\n", - " 'K_X': 0.1,\n", - " 'eta_h': 0.8}" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# For `asm1` and the individual processes within `asm1`, parameters are stored as a dictionary\n", "p1.parameters\n", @@ -839,37 +550,10 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 23, "id": "0ab063bf", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'Y_H': 0.8,\n", - " 'Y_A': 0.24,\n", - " 'f_P': 0.08,\n", - " 'mu_H': 6.0,\n", - " 'K_S': 8.0,\n", - " 'K_O_H': 0.2,\n", - " 'K_NO': 0.5,\n", - " 'b_H': 0.3,\n", - " 'mu_A': 0.5,\n", - " 'K_NH': 1.0,\n", - " 'K_O_A': 0.4,\n", - " 'b_A': 0.05,\n", - " 'eta_g': 0.8,\n", - " 'k_a': 0.05,\n", - " 'k_h': 3.0,\n", - " 'K_X': 0.1,\n", - " 'eta_h': 0.8}" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# If you update parameter values for `p1`, the same parameters will be updated accordingly for\n", "# `asm1` and any other processes in `asm1`.\n", @@ -879,21 +563,10 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 24, "id": "c69a557c", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.2777777777777778" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# If you evaluate process rate or stoichiometry again with the same input, \n", "# you should now expect different output.\n", @@ -911,21 +584,10 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 25, "id": "b7115278", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "('COD', 'charge', 'N')" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "p1.conserved_for" ] @@ -940,20 +602,10 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 26, "id": "772e7e78", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\joy_c\\anaconda3\\envs\\tut\\lib\\site-packages\\qsdsan\\_process.py:499: UserWarning: The following materials aren't strictly conserved by the stoichiometric coefficients. A positive value means the material is created, a negative value means the material is destroyed:\n", - " charge: -5.20417042793042E-18\n", - " warn(\"The following materials aren't strictly conserved by the \"\n" - ] - } - ], + "outputs": [], "source": [ "# No return indicates that all materials in `conserved_for` are conserved.\n", "# Otherwise, a warning or a `RuntimeError` will be raised.\n", @@ -983,21 +635,10 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 27, "id": "b5bde6b2", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(qsdsan._process.Processes,)" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "qs.CompiledProcesses.__bases__" ] @@ -1012,21 +653,10 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 28, "id": "3ee5e0dd", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Let's verify that\n", "isinstance(asm1, qs.CompiledProcesses)" @@ -1042,18 +672,10 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 29, "id": "9f6ae9b6", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Processes([aero_growth_hetero, anox_growth_hetero, aero_growth_auto, decay_hetero, decay_auto, ammonification, hydrolysis, hydrolysis_N])\n" - ] - } - ], + "outputs": [], "source": [ "asm1 = qs.Processes(asm1.tuple)\n", "asm1.show()" @@ -1061,30 +683,12 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 30, "id": "3b1e8036", "metadata": { "scrolled": true }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'aero_growth_hetero': ,\n", - " 'anox_growth_hetero': ,\n", - " 'aero_growth_auto': ,\n", - " 'decay_hetero': ,\n", - " 'decay_auto': ,\n", - " 'ammonification': ,\n", - " 'hydrolysis': ,\n", - " 'hydrolysis_N': }" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Let's see what difference \"decompiling\" made\n", "asm1.__dict__" @@ -1100,191 +704,12 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 31, "id": "48443f1d", "metadata": { "scrolled": true }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'aero_growth_hetero': ,\n", - " 'anox_growth_hetero': ,\n", - " 'aero_growth_auto': ,\n", - " 'decay_hetero': ,\n", - " 'decay_auto': ,\n", - " 'ammonification': ,\n", - " 'hydrolysis': ,\n", - " 'hydrolysis_N': ,\n", - " 'tuple': (,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ),\n", - " 'size': 8,\n", - " 'IDs': ('aero_growth_hetero',\n", - " 'anox_growth_hetero',\n", - " 'aero_growth_auto',\n", - " 'decay_hetero',\n", - " 'decay_auto',\n", - " 'ammonification',\n", - " 'hydrolysis',\n", - " 'hydrolysis_N'),\n", - " '_index': {'aero_growth_hetero': 0,\n", - " 'anox_growth_hetero': 1,\n", - " 'aero_growth_auto': 2,\n", - " 'decay_hetero': 3,\n", - " 'decay_auto': 4,\n", - " 'ammonification': 5,\n", - " 'hydrolysis': 6,\n", - " 'hydrolysis_N': 7},\n", - " '_components': CompiledComponents([S_I, S_S, X_I, X_S, X_BH, X_BA, X_P, S_O, S_NO, S_NH, S_ND, X_ND, S_ALK, S_N2, H2O]),\n", - " '_parameters': {'Y_H': 0.8,\n", - " 'Y_A': 0.24,\n", - " 'f_P': 0.08,\n", - " 'mu_H': 6.0,\n", - " 'K_S': 8.0,\n", - " 'K_O_H': 0.2,\n", - " 'K_NO': 0.5,\n", - " 'b_H': 0.3,\n", - " 'mu_A': 0.5,\n", - " 'K_NH': 1.0,\n", - " 'K_O_A': 0.4,\n", - " 'b_A': 0.05,\n", - " 'eta_g': 0.8,\n", - " 'k_a': 0.05,\n", - " 'k_h': 3.0,\n", - " 'K_X': 0.1,\n", - " 'eta_h': 0.8},\n", - " '_dyn_params': {},\n", - " '_stoichiometry': [[0,\n", - " -1.25000000000000,\n", - " 0,\n", - " 0,\n", - " 1.00000000000000,\n", - " 0,\n", - " 0,\n", - " -0.250000000000000,\n", - " 0,\n", - " -0.0800000000000000,\n", - " 0,\n", - " 0,\n", - " -0.0685997415522571,\n", - " 0,\n", - " 0],\n", - " [0,\n", - " -1.25000000000000,\n", - " 0,\n", - " 0,\n", - " 1.00000000000000,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " -0.0875000000000000,\n", - " -0.0800000000000001,\n", - " 0,\n", - " 0,\n", - " 0.00643122577052393,\n", - " 0.0875000000000000,\n", - " 0],\n", - " [0,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " 1.00000000000000,\n", - " 0,\n", - " -18.0476190476190,\n", - " 4.16666666666667,\n", - " -4.24666666666667,\n", - " 0,\n", - " 0,\n", - " -7.21440615324570,\n", - " 0,\n", - " 0],\n", - " [0,\n", - " 0,\n", - " 0,\n", - " 0.920000000000000,\n", - " -1.00000000000000,\n", - " 0,\n", - " 0.0800000000000000,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " 0.0752000000000000,\n", - " 0,\n", - " 0,\n", - " 0],\n", - " [0,\n", - " 0,\n", - " 0,\n", - " 0.920000000000000,\n", - " 0,\n", - " -1.00000000000000,\n", - " 0.0800000000000000,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " 0.0752000000000000,\n", - " 0,\n", - " 0,\n", - " 0],\n", - " [0,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " 1.00000000000000,\n", - " -1.00000000000000,\n", - " 0,\n", - " 0.857496769403214,\n", - " 0,\n", - " 0],\n", - " [0, 1.0, 0, -1.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", - " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.0, -1.0, 0, 0, 0]],\n", - " '_stoichio_lambdified': None,\n", - " '_rate_equations': (S_NH*S_O*S_S*X_BH*mu_H/((K_NH + S_NH)*(K_O_H + S_O)*(K_S + S_S)),\n", - " K_O_H*S_NH*S_NO*S_S*X_BH*eta_g*mu_H/((K_NH + S_NH)*(K_NO + S_NO)*(K_O_H + S_O)*(K_S + S_S)),\n", - " S_NH*S_O*X_BA*mu_A/((K_NH + S_NH)*(K_O_A + S_O)),\n", - " X_BH*b_H,\n", - " X_BA*b_A,\n", - " S_ND*X_BH*k_a,\n", - " X_BH*X_S*k_h*(K_O_H*S_NO*eta_h/((K_NO + S_NO)*(K_O_H + S_O)) + S_O/(K_O_H + S_O))/(K_X*X_BH + X_S),\n", - " X_BH*X_ND*k_h*(K_O_H*S_NO*eta_h/((K_NO + S_NO)*(K_O_H + S_O)) + S_O/(K_O_H + S_O))/(K_X*X_BH + X_S)),\n", - " '_production_rates': [0,\n", - " -1.25*K_O_H*S_NH*S_NO*S_S*X_BH*eta_g*mu_H/((K_NH + S_NH)*(K_NO + S_NO)*(K_O_H + S_O)*(K_S + S_S)) - 1.25*S_NH*S_O*S_S*X_BH*mu_H/((K_NH + S_NH)*(K_O_H + S_O)*(K_S + S_S)) + 1.0*X_BH*X_S*k_h*(K_O_H*S_NO*eta_h/((K_NO + S_NO)*(K_O_H + S_O)) + S_O/(K_O_H + S_O))/(K_X*X_BH + X_S),\n", - " 0,\n", - " 0.92*X_BA*b_A - 1.0*X_BH*X_S*k_h*(K_O_H*S_NO*eta_h/((K_NO + S_NO)*(K_O_H + S_O)) + S_O/(K_O_H + S_O))/(K_X*X_BH + X_S) + 0.92*X_BH*b_H,\n", - " 1.0*K_O_H*S_NH*S_NO*S_S*X_BH*eta_g*mu_H/((K_NH + S_NH)*(K_NO + S_NO)*(K_O_H + S_O)*(K_S + S_S)) + 1.0*S_NH*S_O*S_S*X_BH*mu_H/((K_NH + S_NH)*(K_O_H + S_O)*(K_S + S_S)) - 1.0*X_BH*b_H,\n", - " 1.0*S_NH*S_O*X_BA*mu_A/((K_NH + S_NH)*(K_O_A + S_O)) - 1.0*X_BA*b_A,\n", - " 0.08*X_BA*b_A + 0.08*X_BH*b_H,\n", - " -0.25*S_NH*S_O*S_S*X_BH*mu_H/((K_NH + S_NH)*(K_O_H + S_O)*(K_S + S_S)) - 18.047619047619*S_NH*S_O*X_BA*mu_A/((K_NH + S_NH)*(K_O_A + S_O)),\n", - " -0.0875*K_O_H*S_NH*S_NO*S_S*X_BH*eta_g*mu_H/((K_NH + S_NH)*(K_NO + S_NO)*(K_O_H + S_O)*(K_S + S_S)) + 4.16666666666667*S_NH*S_O*X_BA*mu_A/((K_NH + S_NH)*(K_O_A + S_O)),\n", - " -0.0800000000000001*K_O_H*S_NH*S_NO*S_S*X_BH*eta_g*mu_H/((K_NH + S_NH)*(K_NO + S_NO)*(K_O_H + S_O)*(K_S + S_S)) + 1.0*S_ND*X_BH*k_a - 0.08*S_NH*S_O*S_S*X_BH*mu_H/((K_NH + S_NH)*(K_O_H + S_O)*(K_S + S_S)) - 4.24666666666667*S_NH*S_O*X_BA*mu_A/((K_NH + S_NH)*(K_O_A + S_O)),\n", - " -1.0*S_ND*X_BH*k_a + 1.0*X_BH*X_ND*k_h*(K_O_H*S_NO*eta_h/((K_NO + S_NO)*(K_O_H + S_O)) + S_O/(K_O_H + S_O))/(K_X*X_BH + X_S),\n", - " 0.0752*X_BA*b_A - 1.0*X_BH*X_ND*k_h*(K_O_H*S_NO*eta_h/((K_NO + S_NO)*(K_O_H + S_O)) + S_O/(K_O_H + S_O))/(K_X*X_BH + X_S) + 0.0752*X_BH*b_H,\n", - " 0.00643122577052393*K_O_H*S_NH*S_NO*S_S*X_BH*eta_g*mu_H/((K_NH + S_NH)*(K_NO + S_NO)*(K_O_H + S_O)*(K_S + S_S)) + 0.857496769403214*S_ND*X_BH*k_a - 0.0685997415522571*S_NH*S_O*S_S*X_BH*mu_H/((K_NH + S_NH)*(K_O_H + S_O)*(K_S + S_S)) - 7.2144061532457*S_NH*S_O*X_BA*mu_A/((K_NH + S_NH)*(K_O_A + S_O)),\n", - " 0.0875*K_O_H*S_NH*S_NO*S_S*X_BH*eta_g*mu_H/((K_NH + S_NH)*(K_NO + S_NO)*(K_O_H + S_O)*(K_S + S_S)),\n", - " 0],\n", - " '_rate_function': None}" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "asm1.compile()\n", "asm1.__dict__" @@ -1300,181 +725,10 @@ }, { "cell_type": "code", - 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S_IS_SX_IX_SX_BH...S_NDX_NDS_ALKS_N2H2O
aero_growth_hetero0-1.25001...00-0.068600
anox_growth_hetero0-1.25001...000.006430.08750
aero_growth_auto00000...00-7.2100
decay_hetero0000.92-1...00.0752000
decay_auto0000.920...00.0752000
ammonification00000...-100.85700
hydrolysis010-10...00000
hydrolysis_N00000...1-1000
\n", - "

8 rows × 15 columns

\n", - "
" - ], - "text/plain": [ - " S_I S_S X_I X_S X_BH ... S_ND X_ND S_ALK S_N2 H2O\n", - "aero_growth_hetero 0 -1.25 0 0 1 ... 0 0 -0.0686 0 0\n", - "anox_growth_hetero 0 -1.25 0 0 1 ... 0 0 0.00643 0.0875 0\n", - "aero_growth_auto 0 0 0 0 0 ... 0 0 -7.21 0 0\n", - "decay_hetero 0 0 0 0.92 -1 ... 0 0.0752 0 0 0\n", - "decay_auto 0 0 0 0.92 0 ... 0 0.0752 0 0 0\n", - "ammonification 0 0 0 0 0 ... -1 0 0.857 0 0\n", - "hydrolysis 0 1 0 -1 0 ... 0 0 0 0 0\n", - "hydrolysis_N 0 0 0 0 0 ... 1 -1 0 0 0\n", - "\n", - "[8 rows x 15 columns]" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# For example, the stoichiometric coefficients of all processes are compiled into a table that\n", "# is in consistent format as a Petersen matrix\n", @@ -1483,88 +737,10 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 33, "id": "18541f64", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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rate_equation
aero_growth_hetero6.0*S_NH*S_O*S_S*X_BH/((S_NH + ...
anox_growth_hetero0.96*S_NH*S_NO*S_S*X_BH/((S_NH ...
aero_growth_auto0.5*S_NH*S_O*X_BA/((S_NH + 1.0)...
decay_hetero0.3*X_BH
decay_auto0.05*X_BA
ammonification0.05*S_ND*X_BH
hydrolysis3.0*X_BH*X_S*(0.16*S_NO/((S_NO ...
hydrolysis_N3.0*X_BH*X_ND*(0.16*S_NO/((S_NO...
\n", - "
" - ], - "text/plain": [ - " rate_equation\n", - "aero_growth_hetero 6.0*S_NH*S_O*S_S*X_BH/((S_NH + ...\n", - "anox_growth_hetero 0.96*S_NH*S_NO*S_S*X_BH/((S_NH ...\n", - "aero_growth_auto 0.5*S_NH*S_O*X_BA/((S_NH + 1.0)...\n", - "decay_hetero 0.3*X_BH\n", - "decay_auto 0.05*X_BA\n", - "ammonification 0.05*S_ND*X_BH\n", - "hydrolysis 3.0*X_BH*X_S*(0.16*S_NO/((S_NO ...\n", - "hydrolysis_N 3.0*X_BH*X_ND*(0.16*S_NO/((S_NO..." - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Similarly for the rate equations\n", "asm1.rate_equations" @@ -1572,21 +748,10 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 34, "id": "a872533e", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.278, 0.03 , 0.179, 0.3 , 0.05 , 0.05 , 2.515, 2.515])" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# More importantly, the `rate_function` attribute of a `CompiledProcesses` now outputs an array\n", "# with each element corresponding orderly to the individual processes\n", @@ -1613,123 +778,10 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 35, "id": "81541f71", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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rate_of_production
S_I0
S_S-1.2*S_NH*S_NO*S_S*X_BH/((S_NH ...
X_I0
X_S0.046*X_BA - 3.0*X_BH*X_S*(0.16...
X_BH0.96*S_NH*S_NO*S_S*X_BH/((S_NH ...
X_BA0.5*S_NH*S_O*X_BA/((S_NH + 1.0)...
X_P0.004*X_BA + 0.024*X_BH
S_O-1.5*S_NH*S_O*S_S*X_BH/((S_NH +...
S_NO-0.084*S_NH*S_NO*S_S*X_BH/((S_N...
S_NH0.05*S_ND*X_BH - 0.076800000000...
S_ND-0.05*S_ND*X_BH + 3.0*X_BH*X_ND...
X_ND0.00376*X_BA - 3.0*X_BH*X_ND*(0...
S_ALK0.0428748384701607*S_ND*X_BH + ...
S_N20.084*S_NH*S_NO*S_S*X_BH/((S_NH...
H2O0
\n", - "
" - ], - "text/plain": [ - " rate_of_production\n", - "S_I 0\n", - "S_S -1.2*S_NH*S_NO*S_S*X_BH/((S_NH ...\n", - "X_I 0\n", - "X_S 0.046*X_BA - 3.0*X_BH*X_S*(0.16...\n", - "X_BH 0.96*S_NH*S_NO*S_S*X_BH/((S_NH ...\n", - "X_BA 0.5*S_NH*S_O*X_BA/((S_NH + 1.0)...\n", - "X_P 0.004*X_BA + 0.024*X_BH\n", - "S_O -1.5*S_NH*S_O*S_S*X_BH/((S_NH +...\n", - "S_NO -0.084*S_NH*S_NO*S_S*X_BH/((S_N...\n", - "S_NH 0.05*S_ND*X_BH - 0.076800000000...\n", - "S_ND -0.05*S_ND*X_BH + 3.0*X_BH*X_ND...\n", - "X_ND 0.00376*X_BA - 3.0*X_BH*X_ND*(0...\n", - "S_ALK 0.0428748384701607*S_ND*X_BH + ...\n", - "S_N2 0.084*S_NH*S_NO*S_S*X_BH/((S_NH...\n", - "H2O 0" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# This matrix operation is already streamlined for `CompiledProcesses` objects, you can see the \n", "# mathematical form of the rates of production as a function of component concentrations.\n", @@ -1738,23 +790,10 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 36, "id": "b508e20f", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0.000e+00, 2.131e+00, 0.000e+00, -2.193e+00, 7.407e-03,\n", - " 1.286e-01, 2.800e-02, -3.292e+00, 7.415e-01, -7.329e-01,\n", - " 2.465e+00, -2.489e+00, -1.264e+00, 2.593e-03, 0.000e+00])" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# To evaluate the rates of production for all components, all you need to\n", "# do is to call the `production_rates_eval` method.\n", @@ -1829,18 +868,10 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 37, "id": "cad5a69b", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CompiledComponents([X_S, S_S, O2, CO2, X_B, H2O])\n" - ] - } - ], + "outputs": [], "source": [ "# Load the default set of components\n", "cmps_all = qs.Components.load_default()\n", @@ -1868,21 +899,10 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 38, "id": "494eb3e8", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Now we can check if their `measured_as` attributes are correctly set\n", "# X_S.measured_as == 'COD'\n", @@ -1892,21 +912,10 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 39, "id": "2de7d166", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "-1.0" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Then you can check relevant `i_` properties of the components\n", "# For example, O2 should have a negative COD content, or more specifically -1 gCOD/gO2\n", @@ -1927,7 +936,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 40, "id": "7b264f76", "metadata": {}, "outputs": [], @@ -1957,7 +966,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 41, "id": "1906bcd6", "metadata": {}, "outputs": [], @@ -1976,7 +985,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 42, "id": "7b7437a4", "metadata": {}, "outputs": [], @@ -2005,7 +1014,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 43, "id": "4f98d569", "metadata": {}, "outputs": [], @@ -2028,7 +1037,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 44, "id": "c7ce74d2", "metadata": {}, "outputs": [], @@ -2049,24 +1058,10 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 45, "id": "e7facea8", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Process: hydrolysis\n", - "[stoichiometry] X_S: -1\n", - " S_S: 1\n", - "[reference] X_S\n", - "[rate equation] X_S*k_hyd\n", - "[parameters] k_hyd: k_hyd\n", - "[dynamic parameters] \n" - ] - } - ], + "outputs": [], "source": [ "# Upon initiation, the parameters are stored as symbols. We still need to set values to them\n", "# before we can evalute process rate.\n", @@ -2075,28 +1070,10 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 46, "id": "9d199cf0", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Process: growth\n", - "[stoichiometry] S_S: -1/y_B\n", - " O2: (y_B - 1.0)/y_B\n", - " CO2: 0.002*(160.0 - 183.0*y_B)/y_B\n", - " X_B: 1.00\n", - "[reference] X_B\n", - "[rate equation] S_S*X_B*mu_B/(K_S + S_S)\n", - "[parameters] y_B: y_B\n", - " mu_B: mu_B\n", - " K_S: K_S\n", - "[dynamic parameters] \n" - ] - } - ], + "outputs": [], "source": [ "# At this point, the initiation of process 3 should be quite straightforward.\n", "# Here shows an alternative way to input stoichiometry\n", @@ -2137,20 +1114,12 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 47, "id": "18d7995b", "metadata": { "scrolled": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CompiledProcesses([hydrolysis, growth, decay])\n" - ] - } - ], + "outputs": [], "source": [ "# Now the final step is to compile the individual processes into a biokinetic model\n", "bkm = qs.Processes([pc1, pc2, pc3])\n", @@ -2160,21 +1129,10 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 48, "id": "0a4159a3", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'k_hyd': k_hyd, 'y_B': y_B, 'mu_B': mu_B, 'K_S': K_S, 'b': b}" - ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Parameters in the stoichiometry and rate equations across all processes are compiled into\n", "# a shared dictionary.\n", @@ -2185,83 +1143,10 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 49, "id": "6bec6d90", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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X_SS_SO2CO2X_BH2O
hydrolysis-110000
growth0-1.25-0.250.03410
decay1000-10
\n", - "
" - ], - "text/plain": [ - " X_S S_S O2 CO2 X_B H2O\n", - "hydrolysis -1 1 0 0 0 0\n", - "growth 0 -1.25 -0.25 0.034 1 0\n", - "decay 1 0 0 0 -1 0" - ] - }, - "execution_count": 56, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# After setting parameter values, the model will be ready\n", "bkm.set_parameters(k_hyd=3.0, y_B=0.8, mu_B=4.0, K_S=9.0, b=0.4)\n", @@ -2270,78 +1155,10 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 50, "id": "f13307e3", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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rate_of_production
X_S0.4*X_B - 3.0*X_S
S_S-5.0*S_S*X_B/(S_S + 9.0) + 3.0*X_S
O2-1.0*S_S*X_B/(S_S + 9.0)
CO20.136*S_S*X_B/(S_S + 9.0)
X_B4.0*S_S*X_B/(S_S + 9.0) - 0.4*X_B
H2O0
\n", - "
" - ], - "text/plain": [ - " rate_of_production\n", - "X_S 0.4*X_B - 3.0*X_S\n", - "S_S -5.0*S_S*X_B/(S_S + 9.0) + 3.0*X_S\n", - "O2 -1.0*S_S*X_B/(S_S + 9.0)\n", - "CO2 0.136*S_S*X_B/(S_S + 9.0)\n", - "X_B 4.0*S_S*X_B/(S_S + 9.0) - 0.4*X_B\n", - "H2O 0" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "bkm.production_rates" ] @@ -2365,91 +1182,14 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 51, "id": "1b8ebfca", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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X_SS_SO2CO2X_BH2OUnnamed: 7
hydrolysis-110000k_hyd*X_S
growth0(-1)/y_B??10mu_B*S_S/(K_S + S_S)*X_B
decay1000-10b*X_B
\n", - "
" - ], - "text/plain": [ - " X_S S_S O2 CO2 X_B H2O Unnamed: 7\n", - "hydrolysis -1 1 0 0 0 0 k_hyd*X_S\n", - "growth 0 (-1)/y_B ? ? 1 0 mu_B*S_S/(K_S + S_S)*X_B\n", - "decay 1 0 0 0 -1 0 b*X_B" - ] - }, - "execution_count": 58, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Stoichiometry and rate equations are usually described in a table format\n", "from qsdsan.utils import load_data\n", - "df_bkm = load_data('_bkm.tsv', index_col=0)\n", + "df_bkm = load_data('assets/_bkm.tsv', index_col=0)\n", "df_bkm" ] }, @@ -2463,22 +1203,14 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 52, "id": "8703f7bf", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CompiledProcesses([hydrolysis, growth, decay])\n" - ] - } - ], + "outputs": [], "source": [ "# The same amount of information is still required to create the model.\n", "bkm_batch = qs.Processes.load_from_file(\n", - " path='_bkm.tsv', \n", + " path='assets/_bkm.tsv', \n", " conserved_for=('COD', 'C'),\n", " parameters=('k_hyd', 'y_B', 'mu_B', 'K_S', 'b'),\n", " compile=True\n", @@ -2488,83 +1220,10 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 53, "id": "4105ea73", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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X_SS_SO2CO2X_BH2O
hydrolysis-110000
growth0-1.0/y_B1.0*(y_B - 1.0)/y_B0.002*(160.0 - 183.0*y_B)/y_B1.000000000000000
decay1000-10
\n", - "
" - ], - "text/plain": [ - " X_S S_S O2 CO2 X_B H2O\n", - "hydrolysis -1 1 0 0 0 0\n", - "growth 0 -1.0/y_B 1.0*(y_B - 1.0)/y_B 0.002*(160.0 - 183.0*y_B)/y_B 1.00000000000000 0\n", - "decay 1 0 0 0 -1 0" - ] - }, - "execution_count": 60, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# You can see `bkm_batch` is equivalent to the `bkm` we created above\n", "bkm_batch.stoichiometry\n", @@ -2574,21 +1233,10 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 54, "id": "ba36c5ed", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'X_S'" - ] - }, - "execution_count": 61, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# The reference component of each process is inferred from its stoichiometry\n", "bkm_batch.decay.ref_component" @@ -2596,26 +1244,14 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 55, "id": "57c80cc6", "metadata": {}, - "outputs": [ - { - "ename": "RuntimeError", - "evalue": "The following materials are unconserved by the stoichiometric coefficients. A positive value means the material is created, a negative value means the material is destroyed:\n C: -0.05", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mRuntimeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_8464\\355048950.py\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# `conserved_for` now applies to all processes\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mbkm_batch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdecay\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcheck_conservation\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;32m~\\anaconda3\\envs\\tut\\lib\\site-packages\\qsdsan\\_process.py\u001b[0m in \u001b[0;36mcheck_conservation\u001b[1;34m(self, rtol, atol)\u001b[0m\n\u001b[0;32m 484\u001b[0m \u001b[0mmaterials\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_conserved_for\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 485\u001b[0m \u001b[0munconserved\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmaterials\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mic_dot_v\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconserved\u001b[0m \u001b[1;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconserved_arr\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mconserved\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 486\u001b[1;33m raise RuntimeError(\"The following materials are unconserved by the \"\n\u001b[0m\u001b[0;32m 487\u001b[0m \u001b[1;34m\"stoichiometric coefficients. A positive value \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 488\u001b[0m \u001b[1;34m\"means the material is created, a negative value \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mRuntimeError\u001b[0m: The following materials are unconserved by the stoichiometric coefficients. A positive value means the material is created, a negative value means the material is destroyed:\n C: -0.05" - ] - } - ], + "outputs": [], "source": [ - "# `conserved_for` now applies to all processes\n", - "bkm_batch.decay.check_conservation()" + "# `conserved_for` now applies to all processes,\n", + "# the following will trigger an error\n", + "# bkm_batch.decay.check_conservation()" ] }, { @@ -2628,21 +1264,10 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 56, "id": "cf5c08b4", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'X_S': 0.32, 'S_S': 0.32, 'O2': 0.0, 'CO2': 1.0, 'X_B': 0.366, 'H2O': 0.0}" - ] - }, - "execution_count": 63, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# `i_C` values for each component\n", "dict(zip(cmps_bkm.IDs, cmps_bkm.i_C))" @@ -2675,7 +1300,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 57, "id": "29c15cb9", "metadata": {}, "outputs": [], @@ -2700,21 +1325,10 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 58, "id": "7fe90104", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'k_hyd': 3.0, 'y_B': 0.8, 'mu_B': 4.0, 'K_S': 9.0, 'b': 0.4}" - ] - }, - "execution_count": 65, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# At this point, the value for `y_B` is not updated yet, since we haven't evalutated it\n", "# with input of component concentrations\n", @@ -2723,30 +1337,10 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 59, "id": "9121e9cc", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Process: growth\n", - "[stoichiometry] S_S: -1/y_B\n", - " O2: (y_B - 1.0)/y_B\n", - " CO2: 0.002*(160.0 - 183.0*y_B)/y_B\n", - " X_B: 1.00\n", - "[reference] X_B\n", - "[rate equation] S_S*X_B*mu_B/(K_S + S_S)\n", - "[parameters] k_hyd: 3\n", - " y_B: 0.8\n", - " mu_B: 4\n", - " K_S: 9\n", - " b: 0.4\n", - "[dynamic parameters] \n" - ] - } - ], + "outputs": [], "source": [ "# But the list of dynamic parameters have been updated\n", "pc2.show()" @@ -2754,21 +1348,10 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 60, "id": "248e0922", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "qsdsan._process.DynamicParameter" - ] - }, - "execution_count": 67, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# `y_B` is now a `DynamicParameter` object stored in the `_dyn_params` attribute of the process\n", "type(pc2._dyn_params['y_B'])" @@ -2776,23 +1359,12 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 61, "id": "9dc27821", "metadata": { "scrolled": false }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'k_hyd': 3.0, 'y_B': 0.5656854249492381, 'mu_B': 4.0, 'K_S': 9.0, 'b': 0.4}" - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Assuming component concentrations are all 1.\n", "state_bkm = np.ones(6)\n", @@ -2804,83 +1376,10 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 62, "id": "d765098e", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
X_SS_SO2CO2X_BH2O
hydrolysis-110000
growth0-1.77-0.7680.210
decay1000-10
\n", - "
" - ], - "text/plain": [ - " X_S S_S O2 CO2 X_B H2O\n", - "hydrolysis -1 1 0 0 0 0\n", - "growth 0 -1.77 -0.768 0.2 1 0\n", - "decay 1 0 0 0 -1 0" - ] - }, - "execution_count": 69, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Then the stoichiometry is updated accordingly\n", "bkm.stoichiometry" @@ -2909,21 +1408,10 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 63, "id": "cb7c404e", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 70, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# The use of `Kinetics` to define process rate is also similar\n", "# For example, for the decay process\n", @@ -2955,21 +1443,10 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 64, "id": "5fdddd88", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1.732, 2.309, 0.369])" - ] - }, - "execution_count": 71, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Define the function\n", "def rhos_eval(state_arr, params):\n", @@ -3001,9 +1478,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [conda env:tut]", + "display_name": "Python 3 (ipykernel)", "language": "python", - "name": "conda-env-tut-py" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -3015,7 +1492,36 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.0" + "version": "3.9.13" + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false } }, "nbformat": 4, diff --git a/docs/source/tutorials/11_Dynamic_Simulation.ipynb b/docs/source/tutorials/11_Dynamic_Simulation.ipynb index aed3ea92..57072991 100644 --- a/docs/source/tutorials/11_Dynamic_Simulation.ipynb +++ b/docs/source/tutorials/11_Dynamic_Simulation.ipynb @@ -3,13 +3,17 @@ { "cell_type": "markdown", "id": "28c4658c", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "source": [ "# Dynamic Simulation \n", "\n", "- **Prepared by:**\n", " \n", - " - [Joy Zhang](https://qsdsan.readthedocs.io/en/latest/authors/Joy_Zhang.html)\n", + " - [Joy Zhang](https://qsdsan.readthedocs.io/en/latest/CONTRIBUTING.html)\n", " \n", "- **Covered topics:**\n", "\n", @@ -19,17 +23,21 @@ " \n", "- **Video demo:**\n", "\n", - " - To be posted\n", + " - [Yalin Li](https://qsdsan.readthedocs.io/en/latest/CONTRIBUTING.html)\n", " \n", - "To run tutorials in your browser, go to this [Binder page](https://mybinder.org/v2/gh/QSD-Group/QSDsan/main?filepath=%2Fdocs%2Fsource%2Ftutorials).\n", + "To run tutorials in your browser, go to this [Binder page](https://mybinder.org/v2/gh/QSD-Group/QSDsan-env/main?urlpath=git-pull%3Frepo%3Dhttps%253A%252F%252Fgithub.com%252FQSD-group%252FQSDsan%26urlpath%3Dtree%252FQSDsan%252Fdocs%252Fsource%252Ftutorials%26branch%3Dmain).\n", " \n", - "You can also watch a video demo on YouTube (link to be posted) (subscriptions & likes appreciated!)." + "You can also watch a video demo on [YouTube](https://youtu.be/1Rr1QxUiE5k) (subscriptions & likes appreciated!)." ] }, { "cell_type": "markdown", "id": "2bc790e7", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ "---\n", "From previous tutorials, we've covered how to use QSDsan's [SanUnit](https://qsdsan.readthedocs.io/en/latest/tutorials/5_SanUnit_advanced.html) and [WasteStream](https://qsdsan.readthedocs.io/en/latest/tutorials/3_WasteStream.html) classes to model the mass/energy flows throughout a system. You may have noticed, the simulation results generated by `SanUnit._run` are **static**, i.e., they don't carry time-related information. \n", @@ -41,13 +49,17 @@ "cell_type": "code", "execution_count": 1, "id": "3dc1138e", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "This tutorial was made with qsdsan v1.2.5 and exposan v1.2.5\n" + "This tutorial was made with qsdsan v1.3.1 and exposan v1.3.1\n" ] } ], @@ -59,15 +71,29 @@ { "cell_type": "markdown", "id": "b7f9ccfc", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "source": [ "## 1. Understanding dynamic simulation with QSDsan \n", "\n", "### 1.1. An example system\n", "Let's use [Benchmark Simulation Model no.1 (BSM1)](http://iwa-mia.org/benchmarking/#BSM1) as an example. BSM1 describes an activated sludge treatment process that can be commonly found in conventional wastewater treatment facilities. The full system has been implemented in [EXPOsan](https://github.com/QSD-Group/EXPOsan/tree/main/exposan/bsm1).\n", "\n", - "The activated sludge process is often characterized as a series of biokinetic reactions in parallel (recap on `Process` [here](https://qsdsan.readthedocs.io/en/latest/tutorials/10_Process.html)). The mathematical models of this kind cannot output mass flows or concentrations directly as a function of input. But rather, they describe the rates of change in state variables at any time as a function of the state variables (often concentrations). As a result, simulation of such systems involves solving a series of ordinary differential equations (ODEs). We have developed features in QSDsan for this specific purpose.\n", - "\n", + "The activated sludge process is often characterized as a series of biokinetic reactions in parallel (recap on `Process` [here](https://qsdsan.readthedocs.io/en/latest/tutorials/10_Process.html)). The mathematical models of this kind cannot output mass flows or concentrations directly as a function of input. But rather, they describe the rates of change in state variables at any time as a function of the state variables (often concentrations). As a result, simulation of such systems involves solving a series of ordinary differential equations (ODEs). We have developed features in QSDsan for this specific purpose." + ] + }, + { + "cell_type": "markdown", + "id": "a8a07c91", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ "#### 1.1.1. Running dynamic simulation" ] }, @@ -75,7 +101,11 @@ "cell_type": "code", "execution_count": 2, "id": "a1c82016", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, "outputs": [ { "name": "stdout", @@ -83,7 +113,7 @@ "text": [ "System: bsm1_sys\n", "ins...\n", - "[0] wastewater\n", + "[0] wastewater \n", " phase: 'l', T: 293.15 K, P: 101325 Pa\n", " flow (kmol/hr): S_I 23.1\n", " S_S 53.4\n", @@ -94,10 +124,10 @@ " S_ND 0.381\n", " ... 4.26e+04\n", "outs...\n", - "[0] effluent\n", + "[0] effluent \n", " phase: 'l', T: 293.15 K, P: 101325 Pa\n", " flow: 0\n", - "[1] WAS\n", + "[1] WAS \n", " phase: 'l', T: 293.15 K, P: 101325 Pa\n", " flow: 0\n" ] @@ -115,178 +145,236 @@ "cell_type": "code", "execution_count": 3, "id": "61ef9ac7", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "outputs": [ { "data": { "image/svg+xml": [ - "\n", + "\n", "\n", - "\n", + "\n", "\n", - "A1CSTR:c->A2CSTR:c\n", - "\n", - "\n", + "A1\n", + "CSTR:c->A2\n", + "CSTR:c\n", + "\n", + "\n", "\n", - " ws1\n", + " ws1\n", "\n", "\n", "\n", - "\n", + "\n", "\n", - "A2CSTR:c->O1CSTR:c\n", - "\n", - "\n", + "A2\n", + "CSTR:c->O1\n", + "CSTR:c\n", + "\n", + "\n", "\n", - " ws3\n", + " ws3\n", "\n", "\n", "\n", - "\n", + "\n", "\n", - "O1CSTR:c->O2CSTR:c\n", - "\n", - "\n", + "O1\n", + "CSTR:c->O2\n", + "CSTR:c\n", + "\n", + "\n", "\n", - " ws5\n", + " ws5\n", "\n", "\n", "\n", - "\n", + "\n", "\n", - "O2CSTR:c->O3CSTR:c\n", - "\n", - "\n", + "O2\n", + "CSTR:c->O3\n", + "CSTR:c\n", + "\n", + "\n", "\n", - " ws7\n", + " ws7\n", "\n", "\n", "\n", - "\n", + "\n", "\n", - "O3CSTR:c->A1CSTR:c\n", - "\n", - "\n", + "O3\n", + "CSTR:c->A1\n", + "CSTR:c\n", + "\n", + "\n", "\n", - " RWW\n", + " RWW\n", "\n", "\n", "\n", - "\n", + "\n", "\n", - "O3CSTR:c->C1Flat bottom circular clarifier:c\n", - "\n", - "\n", + "O3\n", + "CSTR:c->C1\n", + "Flat bottom circular clarifier:c\n", + "\n", + "\n", "\n", - " treated\n", + " treated\n", "\n", "\n", "\n", - "\n", + "\n", "\n", - "C1Flat bottom circular clarifier:c->A1CSTR:c\n", - "\n", - "\n", + "C1\n", + "Flat bottom circular clarifier:c->A1\n", + "CSTR:c\n", + "\n", + "\n", "\n", - " RAS\n", + " RAS\n", "\n", "\n", "\n", - "\n", + "\n", "\n", - "C1Flat bottom circular clarifier:c-> effluent:w\n", - "\n", + "C1\n", + "Flat bottom circular clarifier:c->121356496865:w\n", + "\n", "\n", - " effluent\n", + " effluent\n", "\n", "\n", "\n", - "\n", + "\n", "\n", - "C1Flat bottom circular clarifier:c-> WAS:w\n", - "\n", + "C1\n", + "Flat bottom circular clarifier:c->121356496705:w\n", + "\n", "\n", - " WAS\n", + " WAS\n", "\n", "\n", "\n", - "\n", + "\n", "\n", - " wastewater:e->A1CSTR:c\n", - "\n", - "\n", + "121356497265:e->A1\n", + "CSTR:c\n", + "\n", + "\n", " wastewater\n", "\n", "\n", "\n", - "\n", + "\n", "\n", - "A1CSTR\n", + "A1\n", + "CSTR\n", "\n", - "\n", - "A1CSTR\n", + "\n", + "A1\n", + "CSTR\n", "\n", "\n", "\n", - "\n", + "\n", "\n", - "A2CSTR\n", + "A2\n", + "CSTR\n", "\n", - "\n", - "A2CSTR\n", + "\n", + "A2\n", + "CSTR\n", "\n", "\n", "\n", - "\n", + "\n", "\n", - "O1CSTR\n", + "O1\n", + "CSTR\n", "\n", - "\n", - "O1CSTR\n", + "\n", + "O1\n", + "CSTR\n", "\n", "\n", "\n", - "\n", + "\n", "\n", - "O2CSTR\n", + "O2\n", + "CSTR\n", "\n", - "\n", - "O2CSTR\n", + "\n", + "O2\n", + "CSTR\n", "\n", "\n", "\n", - "\n", + "\n", "\n", - "O3CSTR\n", + "O3\n", + "CSTR\n", "\n", - "\n", - "O3CSTR\n", + "\n", + "O3\n", + "CSTR\n", "\n", "\n", "\n", - "\n", + "\n", "\n", - "C1Flat bottom circular clarifier\n", + "C1\n", + "Flat bottom circular clarifier\n", "\n", - "\n", - "C1Flat bottom circular clarifier\n", + "\n", + "C1\n", + "Flat bottom circular clarifier\n", "\n", "\n", "\n", - "\n", + "\n", "\n", - " wastewater\n", + "121356497265\n", "\n", "\n", - "\n", + "\n", "\n", - " effluent\n", - "\n", + "121356496865\n", + "\n", "\n", - "\n", + "\n", "\n", - " WAS\n", - "\n", + "121356496705\n", + "\n", "\n", "\n", "" @@ -302,15 +390,19 @@ "source": [ "# The BSM1 system is composed of 5 CSTRs in series, \n", "# followed by a flat-bottom circular clarifier.\n", - "sys.diagram()\n", - "# sys.units" + "# sys.units\n", + "sys.diagram()" ] }, { "cell_type": "code", "execution_count": 4, "id": "03c7b593", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "outputs": [], "source": [ "# If we try to simulate it like we'd do for a \"static\" system\n", @@ -320,16 +412,24 @@ { "cell_type": "markdown", "id": "07f91f64", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "source": [ "We run into this error because QSDsan (essentially biosteam in the background) considers this system dynamic, and additional arguments are required for `simulate` to work." ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "id": "b349b9a3", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "outputs": [ { "data": { @@ -337,7 +437,7 @@ "True" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -349,52 +449,64 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "43772dae", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{: True,\n", - " : True,\n", - " : True,\n", - " : True,\n", - " : True,\n", - " : True}" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" + "metadata": { + "slideshow": { + "slide_type": "slide" } - ], + }, + "outputs": [], "source": [ "# This is because the system contains at least one dynamic SanUnit\n", - "{u: u.isdynamic for u in sys.units}\n", + "# {u: u.isdynamic for u in sys.units}\n", "\n", "# If we disable dynamic simulation, then `simulate` would work as usual\n", - "# sys.isdynamic = False\n", - "# sys.simulate()" + "sys.isdynamic = False\n", + "sys.simulate()" ] }, { "cell_type": "markdown", "id": "cc28e85f", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "source": [ "To perform a dynamic simulation of the system, we need to provide at least one additional keyword argument, i.e., `t_span`, as suggested in the error message. `t_span` is a 2-tuple indicating the simulation period.\n", "\n", - ">**Note**: Whether `t_span = (0,10)` means 0-10 days or 0-10 hours/minutes/months depends entirely on units of the parameters in the system's ODEs. For BSM1, it'd mean 0-10 days because all parameters in the ODEs express time in the unit of \"day\".\n", - "\n", + ">**Note**: Whether `t_span = (0,10)` means 0-10 days or 0-10 hours/minutes/months depends entirely on units of the parameters in the system's ODEs. For BSM1, it'd mean 0-10 days because all parameters in the ODEs express time in the unit of \"day\"." + ] + }, + { + "cell_type": "markdown", + "id": "0c111c81", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ "Other often-used keyword arguments include:\n", "\n", "- `t_eval`: a 1d array to specify the output time points\n", "- `method`: a string specifying the ODE solver\n", "- `state_reset_hook`: specifies how to reset the simulation\n", "\n", - "`t_span`, `t_eval`, and `method` are essentially passed to [scipy.integrate.solve_ivp](https://docs.scipy.org/doc/scipy/reference/generated/scipy.integrate.solve_ivp.html) function as keyword arguments. See [documentation](https://biosteam.readthedocs.io/en/latest/API/System.html#biosteam.System.dynamic_run) for a complete list of keyword arguments. You may notice that `scipy.integrate.solve_ivp` also requires input of `fun` (i.e., the ODEs) and `y0` (i.e., the initial condition). We'll learn later how `System.simulate` automates the compilation of these inputs.\n", - "\n", + "`t_span`, `t_eval`, and `method` are essentially passed to [scipy.integrate.solve_ivp](https://docs.scipy.org/doc/scipy/reference/generated/scipy.integrate.solve_ivp.html) function as keyword arguments. See [documentation](https://biosteam.readthedocs.io/en/latest/API/System.html#biosteam.System.dynamic_run) for a complete list of keyword arguments. You may notice that `scipy.integrate.solve_ivp` also requires input of `fun` (i.e., the ODEs) and `y0` (i.e., the initial condition). We'll learn later how `System.simulate` automates the compilation of these inputs." + ] + }, + { + "cell_type": "markdown", + "id": "9c8b4556", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ "---\n", "### Tip\n", "For systems that are expected to converge to some sort of \"steady state\", it is usually faster to simulate with implicit ODE solvers (e.g., `method = BDF` or `method = LSODA`) than with explicit ones. In case of one solver fails to complete integration through the entire specified simulation period, always try with alternative ones.\n", @@ -404,9 +516,14 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "45ef4032", - "metadata": {}, + "metadata": { + "scrolled": true, + "slideshow": { + "slide_type": "slide" + } + }, "outputs": [ { "name": "stdout", @@ -415,10 +532,10 @@ "System: bsm1_sys\n", "Highest convergence error among components in recycle\n", "streams {C1-1, O3-0} after 5 loops:\n", - "- flow rate 7.28e-12 kmol/hr (7.6e-14%)\n", + "- flow rate 1.46e-11 kmol/hr (4.2e-14%)\n", "- temperature 0.00e+00 K (0%)\n", "ins...\n", - "[0] wastewater\n", + "[0] wastewater \n", " phase: 'l', T: 293.15 K, P: 101325 Pa\n", " flow (kmol/hr): S_I 23.1\n", " S_S 53.4\n", @@ -429,7 +546,7 @@ " S_ND 0.381\n", " ... 4.26e+04\n", "outs...\n", - "[0] effluent\n", + "[0] effluent \n", " phase: 'l', T: 293.15 K, P: 101325 Pa\n", " flow (kmol/hr): S_I 22.6\n", " S_S 0.67\n", @@ -439,7 +556,7 @@ " X_BA 0.43\n", " X_P 1.3\n", " ... 4.17e+04\n", - "[1] WAS\n", + "[1] WAS \n", " phase: 'l', T: 293.15 K, P: 101325 Pa\n", " flow (kmol/hr): S_I 0.481\n", " S_S 0.0143\n", @@ -448,12 +565,14 @@ " X_BH 80.3\n", " X_BA 4.69\n", " X_P 14.1\n", - " ... 885\n" + " ... 884\n" ] } ], "source": [ "# Let's try simulating the BSM1 system from day 0 to day 50\n", + "# user shorter time or try changing method to 'RK23' (explicit solver) if it takes a long time\n", + "sys.isdynamic = True\n", "sys.simulate(t_span=(0, 50), method='BDF', state_reset_hook='reset_cache')\n", "sys.show()" ] @@ -461,19 +580,25 @@ { "cell_type": "markdown", "id": "972442da", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "source": [ - "[Back to top](#top)\n", - "\n", "#### 1.1.2. Retrieve dynamic simulation data\n", "The `show` method only displays the system's state at the end of the simulation period. How do we retrieve information on system dynamics? QSDsan uses [Scope](https://qsdsan.readthedocs.io/en/latest/api/utils/scope.html) objects to keep track of values of state variables during simulation." ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "id": "3d7a8b0d", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "outputs": [ { "data": { @@ -481,7 +606,7 @@ "(, )" ] }, - "execution_count": 7, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -494,9 +619,13 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "id": "5fedeb57", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "outputs": [ { "data": { @@ -504,7 +633,7 @@ "" ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -521,13 +650,17 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "id": "a7c7fa4d", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -543,91 +676,97 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "id": "51cc75b4", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "outputs": [ { "data": { "text/plain": [ - "array([[3.000e+01, 5.000e+00, 1.000e+03, ..., 4.152e-11, 9.379e-05,\n", - " 9.223e+04],\n", - " [3.000e+01, 5.000e+00, 1.000e+03, ..., 4.194e-09, 9.473e-03,\n", - " 9.223e+04],\n", - " [3.000e+01, 5.000e+00, 1.000e+03, ..., 8.346e-09, 1.885e-02,\n", - " 9.223e+04],\n", - " ...,\n", - " [3.000e+01, 2.811e+00, 1.146e+03, ..., 2.500e+01, 9.986e+05,\n", - " 9.223e+04],\n", - " [3.000e+01, 2.810e+00, 1.147e+03, ..., 2.501e+01, 9.986e+05,\n", - " 9.223e+04],\n", - " [3.000e+01, 2.810e+00, 1.148e+03, ..., 2.501e+01, 9.986e+05,\n", - " 9.223e+04]])" + "array([], shape=(0, 1), dtype=float64)" ] }, - "execution_count": 10, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Raw time-series data are stored in\n", - "A1.scope.record" + "# A1.scope.record\n", + "A2 = sys.flowsheet.unit.A2\n", + "A2.scope\n", + "A2.scope.record" ] }, { "cell_type": "markdown", "id": "8b8727df", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "source": [ "Each row in the `record` attribute is values of `A1`'s state variables at a certain time point." ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "id": "cab34aab", "metadata": { - "scrolled": true + "scrolled": true, + "slideshow": { + "slide_type": "slide" + } }, "outputs": [ { "data": { "text/plain": [ - "array([0.000e+00, 5.092e-10, 1.018e-09, 6.110e-09, 1.120e-08, 6.212e-08,\n", - " 1.130e-07, 3.163e-07, 5.195e-07, 7.227e-07, 1.402e-06, 2.081e-06,\n", - " 2.761e-06, 8.668e-06, 1.458e-05, 2.048e-05, 3.167e-05, 4.285e-05,\n", - " 5.404e-05, 6.522e-05, 1.049e-04, 1.445e-04, 1.842e-04, 2.238e-04,\n", - " 3.089e-04, 3.940e-04, 4.791e-04, 5.642e-04, 6.493e-04, 8.356e-04,\n", - " 1.022e-03, 1.208e-03, 1.394e-03, 1.581e-03, 1.767e-03, 2.184e-03,\n", - " 2.602e-03, 2.895e-03, 3.188e-03, 3.398e-03, 3.567e-03, 3.736e-03,\n", - " 3.905e-03, 4.038e-03, 4.171e-03, 4.304e-03, 4.437e-03, 4.571e-03,\n", - " 4.704e-03, 4.848e-03, 4.993e-03, 5.138e-03, 5.282e-03, 5.427e-03,\n", - " 5.571e-03, 5.832e-03, 6.092e-03, 6.352e-03, 6.612e-03, 6.872e-03,\n", - " 7.331e-03, 7.790e-03, 8.248e-03, 8.707e-03, 9.408e-03, 1.011e-02,\n", - " 1.081e-02, 1.151e-02, 1.274e-02, 1.396e-02, 1.519e-02, 1.642e-02,\n", - " 1.849e-02, 2.056e-02, 2.196e-02, 2.336e-02, 2.476e-02, 2.616e-02,\n", - " 2.858e-02, 3.100e-02, 3.221e-02, 3.342e-02, 3.463e-02, 3.583e-02,\n", - " 3.704e-02, 3.955e-02, 4.122e-02, 4.290e-02, 4.457e-02, 4.625e-02,\n", - " 5.036e-02, 5.447e-02, 5.858e-02, 6.270e-02, 6.959e-02, 7.649e-02,\n", - " 7.735e-02, 7.821e-02, 7.907e-02, 7.994e-02, 8.083e-02, 8.173e-02,\n", - " 8.262e-02, 8.299e-02, 8.335e-02, 8.367e-02, 8.399e-02, 8.436e-02,\n", - " 8.473e-02, 8.827e-02, 8.872e-02, 8.916e-02, 9.138e-02, 9.359e-02,\n", - " 1.004e-01, 1.072e-01, 1.228e-01, 1.248e-01, 1.267e-01, 1.287e-01,\n", - " 1.291e-01, 1.296e-01, 1.300e-01, 1.344e-01, 1.388e-01, 1.432e-01,\n", - " 1.597e-01, 1.762e-01, 1.927e-01, 1.936e-01, 1.945e-01, 1.954e-01,\n", - " 2.041e-01, 2.128e-01, 2.413e-01, 2.698e-01, 2.983e-01, 2.990e-01,\n", - " 2.997e-01, 3.005e-01, 3.012e-01, 3.083e-01, 3.154e-01, 3.225e-01,\n", - " 3.622e-01, 4.019e-01, 4.416e-01, 5.179e-01, 5.941e-01, 6.703e-01,\n", - " 7.466e-01, 8.335e-01, 9.205e-01, 1.007e+00, 1.094e+00, 1.280e+00,\n", - " 1.466e+00, 1.652e+00, 1.838e+00, 2.148e+00, 2.457e+00, 2.767e+00,\n", - " 3.076e+00, 3.591e+00, 4.106e+00, 4.621e+00, 5.136e+00, 5.874e+00,\n", - " 6.613e+00, 7.351e+00, 8.090e+00, 9.540e+00, 1.099e+01, 1.244e+01,\n", - " 1.389e+01, 1.586e+01, 1.782e+01, 1.979e+01, 2.176e+01, 2.373e+01,\n", - " 2.790e+01, 3.207e+01, 3.624e+01, 4.041e+01, 4.689e+01, 5.000e+01])" + "array([0.000e+00, 5.096e-10, 1.019e-09, 6.115e-09, 1.121e-08, 6.217e-08,\n", + " 1.131e-07, 3.165e-07, 5.198e-07, 7.231e-07, 1.403e-06, 2.082e-06,\n", + " 2.762e-06, 8.671e-06, 1.458e-05, 2.049e-05, 3.168e-05, 4.286e-05,\n", + " 5.405e-05, 6.524e-05, 1.049e-04, 1.446e-04, 1.842e-04, 2.239e-04,\n", + " 3.090e-04, 3.941e-04, 4.793e-04, 5.644e-04, 6.495e-04, 8.358e-04,\n", + " 1.022e-03, 1.208e-03, 1.395e-03, 1.581e-03, 1.767e-03, 2.185e-03,\n", + " 2.602e-03, 2.895e-03, 3.189e-03, 3.398e-03, 3.567e-03, 3.736e-03,\n", + " 3.905e-03, 4.038e-03, 4.171e-03, 4.304e-03, 4.438e-03, 4.571e-03,\n", + " 4.704e-03, 4.849e-03, 4.993e-03, 5.138e-03, 5.283e-03, 5.427e-03,\n", + " 5.572e-03, 5.832e-03, 6.093e-03, 6.353e-03, 6.613e-03, 6.874e-03,\n", + " 7.332e-03, 7.790e-03, 8.248e-03, 8.706e-03, 9.407e-03, 1.011e-02,\n", + " 1.081e-02, 1.151e-02, 1.273e-02, 1.396e-02, 1.519e-02, 1.641e-02,\n", + " 1.848e-02, 2.055e-02, 2.195e-02, 2.335e-02, 2.476e-02, 2.616e-02,\n", + " 2.857e-02, 3.097e-02, 3.218e-02, 3.338e-02, 3.458e-02, 3.578e-02,\n", + " 3.699e-02, 3.949e-02, 4.116e-02, 4.283e-02, 4.450e-02, 4.616e-02,\n", + " 5.025e-02, 5.433e-02, 5.842e-02, 6.250e-02, 6.937e-02, 7.624e-02,\n", + " 7.709e-02, 7.795e-02, 7.881e-02, 7.967e-02, 8.006e-02, 8.045e-02,\n", + " 8.084e-02, 8.182e-02, 8.280e-02, 8.378e-02, 8.420e-02, 8.462e-02,\n", + " 8.503e-02, 8.548e-02, 8.593e-02, 8.620e-02, 8.646e-02, 8.712e-02,\n", + " 8.778e-02, 9.300e-02, 9.822e-02, 1.096e-01, 1.110e-01, 1.124e-01,\n", + " 1.138e-01, 1.144e-01, 1.150e-01, 1.156e-01, 1.171e-01, 1.185e-01,\n", + " 1.200e-01, 1.208e-01, 1.216e-01, 1.224e-01, 1.229e-01, 1.233e-01,\n", + " 1.236e-01, 1.239e-01, 1.255e-01, 1.262e-01, 1.270e-01, 1.328e-01,\n", + " 1.386e-01, 1.519e-01, 1.652e-01, 1.668e-01, 1.685e-01, 1.702e-01,\n", + " 1.717e-01, 1.733e-01, 1.842e-01, 1.856e-01, 1.870e-01, 1.878e-01,\n", + " 1.886e-01, 1.891e-01, 1.896e-01, 1.899e-01, 1.902e-01, 1.933e-01,\n", + " 1.965e-01, 2.170e-01, 2.375e-01, 2.581e-01, 2.592e-01, 2.603e-01,\n", + " 2.614e-01, 2.723e-01, 2.832e-01, 3.164e-01, 3.496e-01, 3.828e-01,\n", + " 4.503e-01, 5.178e-01, 5.852e-01, 6.527e-01, 7.282e-01, 8.037e-01,\n", + " 8.791e-01, 9.546e-01, 1.105e+00, 1.256e+00, 1.406e+00, 1.557e+00,\n", + " 1.810e+00, 2.063e+00, 2.317e+00, 2.570e+00, 3.003e+00, 3.436e+00,\n", + " 3.869e+00, 4.302e+00, 4.915e+00, 5.528e+00, 6.142e+00, 6.755e+00,\n", + " 7.995e+00, 9.236e+00, 1.048e+01, 1.172e+01, 1.341e+01, 1.511e+01,\n", + " 1.680e+01, 1.850e+01, 2.118e+01, 2.386e+01, 2.654e+01, 2.923e+01,\n", + " 3.427e+01, 3.932e+01, 4.437e+01, 4.942e+01, 5.000e+01])" ] }, - "execution_count": 11, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -640,7 +779,11 @@ { "cell_type": "markdown", "id": "c14d81b0", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "source": [ "The tracked time-series data can be exported to a file in two ways." ] @@ -649,7 +792,11 @@ "cell_type": "code", "execution_count": 13, "id": "c126483f", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "outputs": [], "source": [ "# sys.scope.export('bsm1_time_series.xlsx')\n", @@ -667,16 +814,24 @@ { "cell_type": "markdown", "id": "5b93411d", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "source": [ "We can also (re-)define which unit or stream to track during dynamic simulation." ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 14, "id": "b818bfbf", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "outputs": [ { "data": { @@ -684,7 +839,7 @@ "(, )" ] }, - "execution_count": 12, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -699,15 +854,18 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 15, "id": "f6b35327", "metadata": { - "scrolled": false + "scrolled": false, + "slideshow": { + "slide_type": "slide" + } }, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -718,21 +876,25 @@ ], "source": [ "# Need to rerun the simulation before retrieving results\n", + "# user shorter time or try changing method to 'RK23' (explicit solver) if it takes a long time\n", "sys.simulate(t_span=(0, 50), method='BDF', state_reset_hook='reset_cache')\n", "fig, ax = C1.scope.plot_time_series([f'TSS{i}' for i in range(1,11)])" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 16, "id": "68dcbad5", "metadata": { - "scrolled": false + "scrolled": false, + "slideshow": { + "slide_type": "slide" + } }, "outputs": [ { "data": { - "image/png": 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ycnKDjqlUKuFwOGCz2Rhag1hDQ4Isy8gvseH0pTKcyi/F6fxSnLlUitP5Zfg5twh5RbXXlnRxSDIuViqZFK5XIzJMg1ZhIWgVprl8C7m8TIPIsBCcyS/Fwx8cdvuaJvdv59XYzqb82Q6UHpa6JCXEYnR8jN98UHsjkNsOOP9f+tsYZU8Fctubiz+9R/7Ultr41RjgYOWrD7OnnnoKsixXDFfo2LEjXn75ZcybNw/jxo3zaCjExYsXkZOTA4fDgWPHjmH58uUYMWJEjZ5yCh7uhim8ekc/9IgxVgTc05cDrut+qe3qhuc/PvYaTLm2HcJDQ6BWKtxu37edGS9+9WOTjO1syp9tBjHfCeS2E5ETA7CfaO4Ps2+//Ravv/46vvnmmyrVMB588EF8+umnuO+++zwaCjFq1CgAzp7f2NhYjB8/vt4JNKhlc1f6CwAecdPbKghAtEGL9hE6tI/Qo324Hu0j9LCU2bBoQ6bbNgyIC0eU0fO/PjT1X2Ga8mebQYyIqGF8OgY4UDTXVMhUFd/b5nG147SsdhE/5xbhh3MWbMvMxdZ6LhZzCQ1RonPrMGfIDdejXYQe7cN1iIvQo224DhpVzYs5m3q8fKCP7SQiCnYBMwaYiHzL29BXZLUj45wFP1TcCvFrXjEcXl5x9sLk3rilX1uv9gnknloiIvIvDMBUq1mzZuHdd9+tdd20adOwatWqZm4RNTZ3Y3WX3tYbUUbt5cBbiB/OWfD7xdqrB4br1ejVxoTwUDW+OJLt9rkbOgtQU4+X55ACIqLgwABMtVq0aBHmzZtX6zpe4Bb4PBmr+8Qnx2rdt61Zh/g2RvRqY0SvNib0amNErEkLQRAgSjL2n7zUpJMFsKeWiIiuFgMw1SoqKgpRUVG+bgZ5oCFjeL/9+Xy90/S6tDFpMbBjBHq1MSKhrQnxsUaEh4bUuX1zlfVjTy0REV0NBmCiAObpGF6HKOHImULs/uUCvvv1Avb/7n4qXQB4YlwPr8fqBnqNWiIiavkYgIkClLsxvAsmxUMpCNj1ywXs/e0iiqwOr5/jasbqcpgCERH5KwZgogDkyRjeheszqiw36dRI7BqJxK6tMKxzK/xp9b4mHavLYQpEROSvGICJAlBaVr5HY3jjY42Y0CcW13dthYS2pio9sL6agpuIiMjX3M8TSkR+paTcgY3Hznm07YN/6Iy/jOiKvu3NNcKsa6xujKnqMIcYkxYrpw3gWF0iImqx2APsTyQR+H0PUJwLhEUDHYYBipozYlHL4WkFB4co4bsTF/G/Q2ex+YcclNpEj47vbgwvx+oSEVEwYgD2FxnrgU1PAJZKPXvGNkDSS0D8pEZ/OlEUccMNNyAmJgaffvppxfLCwkIkJCRg+vTpWLJkSZ37nzx5Ep06dap4rFarERcXhxkzZmD+/PkQhKoB6syZM+jcuTO6d++O48ePN/rrCUTuKjjIsowfzlnw2aGzWH/kHM4XlVds1yFCh4sldhSX135hmzdjeDlWl4iIgg0DsD/IWA98NB2ofjmSJdu5/PZ3Gj0EK5VK/Oc//0G/fv2wbt063HXXXQCAlJQUREREYMGCBR4dZ+vWrejVqxfKy8uxe/duzJw5E7GxsbjvvvuqbPef//wHt99+O3bu3Invv/8eQ4YMadTXE2jqq+Aw692DuKVfG2Scs+CXvOKKdeF6NSb2bYNb+7dF//ZmbP4hB8nvHgTAMbxERETeYABuCrIM2GufMrYGSQS++itqhF/ngQAIzp7hzjd6NhxCrQcEz0JP9+7dsXTpUqSkpOCmm25CWloaPvjgA6SnpyMkpO7JDiqLjIxETEwMAKBDhw54++23cfDgwSoBWJZlvP3223jjjTfQrl07rFmzJqgDsCcVHD4/7PxLQIhKgdE9o/H/+rfF8O6tEaK6Mmyf9XaJiIgahgG4KdhLgRfaNNLBZOewiKXtPdv86XNASKjHR09JScFnn32Gu+++G8eOHcOzzz6Lvn37Nqil+/fvx4EDBzB9+vQqy3fs2IHS0lKMGjUKbdu2xbBhw/DKK68gNNTzdrYknlZweGB4Jzx0UzcYteo6t+EYXiIiIu8xAAc5QRCwcuVK9OzZE71798aTTz7p1f7Dhg2DQqGAzWaD3W7HAw88UCMAr1mzBnfccQeUSiUSEhLQuXNn/Pe//8WMGTMa8ZUEjrwi9+EXAHq1MdUbfl04hpeIiMg7DMBNQa139sR64vc9wLo/ut/uro+dVSE8eW4v/fvf/4Zer0dWVhbOnDmDjh07erzvhx9+iJ49e8Jut+P48eNISUlBeHg4li5dCgAoKCjAp59+it27d1fsM23aNKxZsyYoA/CveUX4KP20R9s2dBY2IiIiqh8DcFMQBM+HIXS5yVntwZKN2scBC871XW5qkpJoe/bswSuvvIKvv/4aixcvxn333YetW7fWqOJQl/bt26Nr164AgJ49e+LEiRN45plnsHDhQmi1Wrz33nuwWq1VxvzKsgxJkvDzzz+je/fujf6a/FFmtgUrtv+KjcezIdd2mitpjFnYiIiIqG6cCMPXFEpnqTMAV67fR9XHSUubJPyWlpZixowZSE5OxogRI7BmzRqkpaVh1apVDT6mUqmEw+GAzWYD4Bz+8Nhjj+Hw4cMVtyNHjuCGG27Av//978Z6KT4jSjL2nriIzw+fxd4TFyFKVdPt0TMFuP+d/Ri3fBe+POYMv2N7RePpcT0goM4zzgoORERETYg9wP4gfpKz1FmtdYCXNkkdYAB46qmnIMtyxXCFjh074uWXX8a8efMwbtw4j4ZCXLx4ETk5OXA4HDh27BiWL1+OESNGwGg04vDhwzh48CDWrVuHHj16VNnvzjvvxKJFi7B48WKoVIH537C+Or6tDRr8c9uv+Pbn8wCcfxSY0DsWD93UFT1ijACAuEg9KzgQERH5gCDLssXXjfB3hYWFMJvNhsLCQhiNxirrrFYrsrKy0KlTJ2i1Vzlmsxlngvv2228xcuRIfPPNN7j++uurrBs7diwcDke9QyGqT4ShVCoRGxuLcePGYcmSJWjdujVSUlKwfft2/PDDDzX2z8nJQdu2bfHZZ59h0qTaA36jvreNrK46vtUpFQJu6dsGs0d0RdeosBrrPZ0JjoiIiOpnsVhgMplQUFBQZDKZ6t2WAdgDzRaAqQp/fW9FScb1L213W8psyrXt8NBNXdEhMjjLvRERETUnbwIwxwATecnTOr6TB7Rj+CUiIvJDDMBUq1mzZiEsLKzW26xZs3zdPJ/ytI6vp9sRERFR8wrMq4+oyS1atAjz5s2rdV31YSDBRpLcjfx1Yh1fIiIi/8QATLWKiopCVFSUr5vhV0RJxtvfZWHZph/r3Y51fImIiPwbA3Ajkd3NbkBe86f39LfzxXj846M48PslAECPGAN+zCmCgKrTl7COLxERkf9jAL5KarUagHNSCZ1O5+PWtCylpaUArrzHvuDq9f3b5p9Q7pAQplFh/oSeuGNQe2z+IYd1fImIiAIQA/BVUiqVMJvNyMvLAwDo9XqPpxGm2smyjNLSUuTl5cFsNkOpbJpayO6cOF+Mx/97BAdPFQAAbujWCktv64O2ZucXnaSEWIyOj2EdXyIiogDDANwIYmJiAKAiBFPjMJvNFe9tU6ltIgoAWLP7N7z89c+wXe71TZ3QE1MHta/x5UapEDC0S2STtpGIiIgaFwNwIxAEAbGxsYiKioLdbvd1c1oEtVrd5D2/tU1l3CosBAatClkXnMMvhndvjRcn967o9SUiIqLAxwDciJRKpc/+XE/eqWsq4wvFNlwotkGrUuC5W3rh9oE1e32JiIgosHEiDAo6oiTjuS8yaoTfyow6Nf54LcMvERFRS8QATEHHk6mM84rKkZaV30wtIiIioubEAExBh1MZExERBTcGYAo6Zr1ndYU5lTEREVHLxIvgqMWqrcTZxeJy/P3rn+vdj1MZExERtWwMwNQi1VXizCHJKCi1Qx+iRKlN5FTGREREQYhDIKjFcZU4q36h24ViGwpK7Yg2arDx4RuwatoAxJiqDnOIMWmxctoATmVMRETUgrEHmFoUT0qcAUD7CD06tgrlVMZERERBiAGYWhRPSpzlWpwlzoZ2ieRUxkREREGIQyCoRWGJMyIiInKHAZhaFE9Ll7HEGRERUfBiAKYWZWCHcOjUyjrXCwBiWeKMiIgoqHEMMAWs6nV+B3YIx/z/HUOZXax1e5Y4IyIiIoABmAJUbXV+dWolyuwiFAIwY1hHfHU8p8r6GJMWCybGs8QZERFRkGMApoDjqvNbvdSZq+f3nsROeObmeMyfEM8SZ0RERFQDAzAFFE/q/G48lo2nx/dkiTMiIiKqFS+Co4DiSZ3f7EIr0rLym6lFREREFGgYgCmgsM4vERERXS0GYAoorPNLREREV4sBmALK4E4RiAgNqXM96/wSERGROwzAFFB+O1+MUpuj1nWs80tERESe8JsAvHjx4hBBEAwpKSka17KysjLMmjVLGxERERYWFma49dZbddnZ2VWSzcmTJ4WkpCSdXq83tG7dOmzOnDkau91e5djbtm1T9uvXL1Sj0Ri6dOkStnr1anUzvSxqROeLyjHj7XRY7RK6tA5FjFFTZX2MSYuV0wawzi8RERHVyy/KoO3bt0+xevXqkISEBKny8kceeUT71VdfqT788MMyk8kkp6SkaCdPnqzbu3dvKQA4HA5MmDBBHx0dLe/ataskOztbmDFjhk6tVmPZsmXlAHDixAlh0qRJ+vvvv9+2bt06+5YtW5SzZs3StmnTRho/fnztU4aRX6g805tZp8bft/yMswVl6Bipx39nDYNJp2adXyIiIvKaIMuyxZcNKCoqwoABA0JXrFhhXbJkiaZv377ia6+9Vl5QUICoqCjD2rVry6ZOneoAgIyMDEWvXr1Cd+/eXZqYmChu2LBBecstt+jPnDlTHBsbKwPAihUr1PPnz9fm5eUVaTQaPPbYY5qvvvpKlZGRUeJ6zilTpugKCgqELVu2lHrSxsLCQpjNZkNhYSGMRmPTvBFURW0zvQGAPkSJLx++AZ1ahfqoZUREROSPLBYLTCYTCgoKikwmU73b+nwIRHJysnbcuHGOsWPHVumNTU9PV9rtdowZM6ZiwGd8fLzUvn17ec+ePUoA2LNnj6pXr16SK/wCwLhx4xwWiwXHjh1TAMD333+vHDlyZJVBo2PGjHGkpaUp62qT1WpFYWFhxc1i8el3hKDjmumttnq/pTYRP+XwfBAREVHD+TQAr1u3TnXo0CHlSy+9VF59XU5OjhASEoLw8PAqy6OiouScnBwBAHJzc4WoqKgqk4LFxMTIrv3r28ZisaC0tPYO4MWLF2vMZrPBdYuLizNczeskz7mb6U0A8NwXGRCl+uaCIyIiIqqbzwLw77//LsyZM0e7bt26Mp1O56tm1Co1NbW8oKCgyHU7depUka/bFCzczfQmgzO9ERER0dXxWQDev3+/8vz588LAgQNDVSqVQaVSGXbt2qV8/fXXQ1QqlSE6Olq22Wy4dOlSlf3y8vIEVy9vdHS0nJeXV+WqJ1fPr7ttjEYj9Hp9rW3TarUwmUwVN477bT6c6Y2IiIiams8C8OjRox1HjhwpOXjwYMVtwIAB0h133GE/ePBgyeDBg0W1Wo0tW7ZUVKrIzMxUnD59Whg2bJgIAMOGDXP88MMPClfoBYDNmzerjEYjXBUlhgwZIm7fvr1KtYutW7eqBg8ezAoQfogzvREREVFT81kZNKPRiD59+lQpexYaGipHRkbKruUzZsywz5s3TxsREVFmNBrlhx9+WDtkyBAxMTFRBICkpCSxR48e0rRp03TLli2z5uTkCAsWLNA8+OCDNq3WGZBmz55tW7VqVcjcuXM1M2fOtG/dulX5ySefqNavX+9RBQhqXoM7RcCoVcFirXuyixjO9EZERERXwedVIOqzfPly6/jx4x233367fsSIEaHR0dHyp59+WuZar1KpsGHDhlKlUilff/31odOnT9dNmzbNvmTJkoqL6rp06SKvX7++dNu2bar+/fuHvvrqq5pVq1ZZWQPYP/2UU4RSW+2nhjO9ERERUWPweR3gQMA6wM2j1ObAxNd248T5EiS0MeJCiQ05lS6IizVpsWBiPGd6IyIiohq8qQPsFzPBEQHAoi8ycOJ8CaIMGvzfvYNh1odwpjciIiJqdAzA5BOVpzmOMmiRZ7Hig/TTEATg1an9EBmmAQAM7RLp45YSERFRS8MATM2utmmOXf26s2/sgmFdW/mmYURERBQUGICpWbmmOa4+j5vrcc9YjrEmIiKipuXXVSCoZXE3zTEALPkyk9McExERUZNiAKZm426aY4DTHBMREVHTYwCmZsNpjomIiMgfMABTs+E0x0REROQPGICp2QzuFIFYU93hVoBzsgtOc0xERERNiQGYmo1SISB1Qs9a13GaYyIiImouLINGTa7ypBfpJ50XuAlAlWoQMZzmmIiIiJoJAzA1qdomvQCAOwe3x8S+bTnNMRERETU7jwLwZ5995nVQHjt2rEOv13vfImox6pr0AgDeTzuN4d1b45Z+bZu9XURERBTcPAq2t912m86bgwqCgJ9++qm4a9eunNEgSHky6cVzX2RgdHwMe36JiIioWXl8Edy5c+eKJUkq8uTGnl9yN+mFDE56QURERL7hUQCeNm2aXa/Xe9ybe+edd9pNJlPDW0UBj5NeEBERkb/yaAjEO++841VKefPNN5lqghwnvSAiIiJ/1Wh1gDMyMhTdunULbazjUWDjpBdERETkrxotAFutVvz222+cWIMAOCe9uH1g+1rXcdILIiIi8iUGVmoSoiRj8w85AAB9iLLKuhiTFiunDeCkF0REROQTnAiDmsRH+0/jx5wimHRqbH/sD/g5t5iTXhAREZFfYACmRuOa8vhUfgle3JgJAHhkZDdEhmkwNEzj49YREREROXkcgMPDww2CUHevncPhaJQGUWCqbcpjpUJAlJHBl4iIiPyLxwH473//O0ubUa3qmvJYlGSkvHcIKoXA8b5ERETkNzwOwMOHD3dwamOqjlMeExERUaDxuApEv379wuLj40P/+te/avbu3cvqEQSAUx4TERFR4PE4yJ4/f77ohRdeKD9//rxw66236mNiYsLuvfde7f/+9z9VWVlZU7aR/BinPCYiIqJA43EA1ul0uPXWWx1vv/22NTs7u/i///1vWUREhPzkk09qWrdubZg4caLuzTffVOfm5vLv3EGEUx4TERFRoGnQUAaFQoEbbrhBfPnll8t//PHHkgMHDpRcf/314jvvvKOOi4sLW758ubqxG0r+iVMeExERUaBplLG811xzjfTEE0/Ydu/eXXr27NnipKQksTGOS/5PqRBw93Udal3HKY+JiIjIH3k9EcZnn31W6z6CIECr1crdu3eXrrnmGunqm0aBIu2k8wI3nVqJMvuV7z4xJi0WTIxnCTQiIiLyK14H4Ntuu00nCAJkuWrhK9cyQRAwbNgw8fPPPy+NiOCfvVu6g6cu4ZufzkOpEPDlw9cj11LOKY+JiIjIr3k9BGLTpk2l1157rbhp06bSgoKCooKCgqJNmzaVDho0SPz888/LduzYUXrx4kVh7ty5vOqphRIlGXtPXMTnh89i4fofAACT+7dF59ZhGNolErf0a4uhXSIZfomIiMgved0D/Oijj2r/9a9/WW+44YaKv3WPGTNG1Gq15Q8++KA2MzOz5JVXXrHOnDlT17hNJX9Q25THANCnndk3DSIiIiLyktcBOCsrS2EymWpM/GUymeSTJ08qAKB79+7SxYsX2f3XwtQ15TEAPPv5cbQ2hHC8LxEREfk9r4dA9O/fX5w3b562cr3f3Nxc4fHHH9dee+21IgD8/PPPinbt2vFCuBbE0ymPRYmzZRMREZF/8zoAr1mzxnry5EkhLi4urEuXLmFdunQJi4uLC/v999+F1atXWwGguLhYePrpp22N31zyFU55TERERC2F10MgevbsKWVmZpZs2rRJ+dNPPykBoEePHuLYsWNFpVIJALjtttscjdxO8jFOeUxEREQthdcBGACUSiUmTJggTpgwgRNeBAlOeUxEREQtRYMC8L59+xTbt29XnT9/XpCkqkN9ly9fXt4oLSO/4pryuK5hEAKcE19wymMiIiLyd14H4EWLFoUsXLhQ061bNyk6OloWhCvFHirfp5ZFqRCwYGI8Zr17sMY6TnlMREREgcTrALxixYqQN9980zpz5kx7UzSI/NcN3VpDp1agzF61159THhMREVEg8ToAKxQKVJ4Eg4LHJwfPoMwuoUOEDi9M7oMLxeWc8piIiIgCjtdl0B5++GHbihUr1E3RGPJfoiTj37uzAAD33dAZiV1bccpjIiIiCkhe9wA/8cQTtnHjxuk7d+4c1qNHD1GtrpqFP//887JGax35ja2ZuTh5sRQmnRp/vLadr5tDRERE1GBeB+CHHnpIu3PnTuXw4cPFyMhImRe+tVyiJCMtKx95RVas3HECAPCnIXHQhzSoeAgRERGRX/A6ybz77rvqjz76qGzSpEmc7KIF23Q8G899kVGj7FlchN5HLSIiIiJqHF4H4PDwcLlr166S+y0pUG06no3kdw9CrmXd058eQ7hezYoPREREFLC8vgjumWeeKX/22Wc1JSUlTdEe8jFRkvHcFxm1hl+X577IgCjVtwURERGR/2pQHeCsrCxFTEyMIS4uTqp+Edzhw4eZjANYWlZ+nbO9AYAMILvQirSsfAztEtl8DSMiIiJqJF4H4EmTJnECjBYsr6ju8NuQ7YiIiIj8jdcB+Pnnn7c1RUPIP0QZtI26HREREZG/8XoMMLVsgztFINakRV3F7QQAsSbn7G9EREREgcijABwREWE4f/68xwV/27dvH5aVlcUCwQFIqRCwYGJ8rRfBuU7ogonxnP2NiIiIApZHQyAKCgrw5Zdfqkwmk0eX/ufn5wuiKF5dy8gnREmGSReCHjEG/JhTVGVdjEmLBRPjWQKNiIiIAprHY4DvvffeRh/0+dprr6n/9a9/hZw6dUoBAD179hSfeeaZ8ptvvlkEgLKyMsyZM0f70UcfqWw2mzBq1CjHypUrrbGxsRVB/OTJk8KsWbO0O3fuVIWGhsrTpk2zL1u2rLxydYpt27YpH3vsMW1mZqaiXbt28lNPPVU+c+ZMXsxXTW2TXxi0Ktx+bTuMio/B4E4R7PklIiKigOfREAhJkoq8vXXt2tVtb3H79u3lF198sTw9Pb0kLS2t5MYbbxQnT56sP3bsmAIAHnnkEe2XX36p+vDDD8u2b99ekp2dLUyePFnn2t/hcGDChAl6m80m7Nq1q+Ttt98uW7t2rXr+/Pka1zYnTpwQJk2apL/xxhsdBw8eLElJSSmfNWuWduPGjcqGvGEtlWvyi+ol0IqtDvz7u5MoLLMx/BIREVGLIMiybPF1IyqLiIgwvPjii9apU6fao6KiDGvXri2bOnWqAwAyMjIUvXr1Ct29e3dpYmKiuGHDBuUtt9yiP3PmTLGrV3jFihXq+fPna/Py8oo0Gg0ee+wxzVdffaXKyMioqE88ZcoUXUFBgbBly5ZST9pUWFgIs9lsKCwshNFobJoX7kOiJOP6l7bXWf9XgHP4w+4nbmIIJiIiIr9ksVhgMplQUFBQZDKZ6t3Wb6pAOBwOrFu3TlVSUoLExEQxPT1dabfbMWbMGIdrm/j4eKl9+/bynj17lACwZ88eVa9evaTKQyLGjRvnsFgscPUif//998qRI0c6Kj/XmDFjHGlpaXX2AFutVhQWFlbcLBa/+o7Q6LyZ/IKIiIgo0Pk8AB85ckQRFhZm0Gq1hr/85S+6jz/+uCwhIUHKyckRQkJCEB4eXmX7qKgoOScnRwCA3NxcISoqqspQi5iYGBkA3G1jsVhQWlp7B/DixYs1ZrPZ4LrFxcUZGu8V+x9OfkFERETBxOcBuEePHtLBgweL9+zZU3L//ffb7rnnHu3x48d92q7U1NTygoKCItft1KlTRe73Clyc/IKIiIiCidczwTU2jUaD7t27ywDkwYMHl+/fv1/56quvhkydOtVus9lw6dKlKr3AeXl5gquXNzo6Wk5PT68yKNXV81t5m7y8vBrbGI1G6PX6Wtuk1Wqh1QZP2HNNfuFuDDAnvyAiIqKWoEE9raIo4scff1R8++23yh07dlS5XW2DZFlGeXk5Bg0aJKrVamzZsqUipGdmZipOnz4tDBs2TASAYcOGOX744QeFK/QCwObNm1VGoxEJCQkSAAwZMkTcvn17laC/detW1eDBg1mo+DLX5Be14eQXRERE1NJ43QP83XffKadNm6Y7deqUIMtVK50JggBRFD0eLvD4449rxo8f7+jQoYNUVFQkvPvuu+qdO3cqN27cWG42mzFjxgz7vHnztBEREWVGo1F++OGHtUOGDBETExNFAEhKShJ79OghTZs2Tbds2TJrTk6OsGDBAs2DDz5oc/Xgzp4927Zq1aqQuXPnambOnGnfunWr8pNPPlGtX7/eowoQLZ0oyUjLykdeUXmt6zn5BREREbU0Xgfg5ORk7YABA8QNGzaUt2nTRhKEhvcKnj9/XpgxY4bu8pAEOSEhQdq4cWNpUlKSCADLly+3zpkzR3v77bfry8vL4ZoIo6LxKhU2bNhQOmvWLO31118fqtfr5bvvvtu+ZMmSijTXpUsXef369aVz587Vvv766yFt27aVV61aZR0/fnzQ9wDXNvGFSgGk3NQNHVuFIsqg5eQXRERE1OJ4XQc4NDTUcOjQoeLL43aDQkusA+ya+KK2kygAWDltAHt9iYiIKGA0aR3gQYMGib/88ovPq0dQw4mSjOe+yKg1/Lo890UGRClovuMQERFREPF6CMRDDz1kmzdvnjY7O9vWp08fMSQkpMr6fv36SY3WOmoS3kx8MbRLZPM1jIiIiKgZeB2Ab7/9dh0APPDAAxV1wgRBgCzLXl8ER77BiS+IiIgomHkdgE+cOFHcFA2h5sOJL4iIiCiYeR2AO3XqxIGhAc418UVOobXOi+A48QURERG1VA26mO2XX34RZs+erR0xYoR+xIgR+r/85S+aX375hbWyAgQnviAiIqJg5nUA3rhxozIhISEsPT1d0adPH7FPnz5iWlqasnfv3mGbNm266pngqHkkJcTinsSONZbHmLQsgUZEREQtmtdDIJ566iltSkqK7eWXX64yddi8efM0Tz75pDYpKamk8ZpHTcE1+9v3WfkAgFv7tcGIHlGc+IKIiIiCgtc9wD/99JPi/vvvt1dfPnPmTPuPP/7I+sB+btPxbFz/0nbc+dY+/HDOOQfK7l8vQKNSYGiXSIZfIiIiavG8DqytWrWSDx06VGO/Q4cOKVq3bs0L5PyYa/a36jWALxbbkPzuQWw6nu2jlhERERE1H6+HQNx777225ORk3YkTJ8oTExNFANi9e7fy73//u+bhhx8ud7c/+UZ9s7/JcF789twXGRgdH8NeYCIiImrRvA7ACxcutBkMBrz66qshzzzzjAAAsbGxcmpqavmcOXNsjd9Eagyc/Y2IiIjIyesArFAo8Pjjj9sef/xxm8XiHENqNBobvWHUuDj7GxEREZGT1wG4MgbfwMHZ34iIiIicPArA/fr1C92+fXtJREQE+vbtGyoIdY8RPXz4MMug+SHX7G91DYPg7G9EREQULDwKwBMnTrRrNBrXfYcgCKz2EGBcs7/NevdgjXWc/Y2IiIiCiSDLssXXjfB3hYWFMJvNhsLCwoAf9pH06k78mFNUZVmsSYsFE+M5+xsREREFLIvFApPJhIKCgiKTyVTvtl6PAe7UqVNYenp6SatWrar0Al+6dAkDBgwIy8rKKvb2mNQ8LpXY8Eue8/S8OrUvBEHg7G9EREQUdLwOwL///rvgcDhqLLdarcLZs2eZovyQa+rj9UfOQpRk9Igx4Nb+7XzdLCIiIiKf8DgAf/bZZxXbbtq0SWUymSp6gEVRxLZt21QdO3aUGruBdHU2Hc/Gc19kVLn47cylMmw6ns0hD0RERBSUPB4DrFAoDAAgCAJkueo1cGq1Gh06dJD+9re/ld9yyy01u4cDXKCOAXZNfVzbFYsCgJXTBjAEExERUYvQJGOAJUkqAoCOHTuGpaenl7Ru3ZqVIPxYfVMfu3DqYyIiIgpGCm93OHnyZDHDr//zZupjIiIiomDSoJngiouLsWPHDtXvv/8u2Gy2Kt2Hc+fOtTVO0+hqcOpjIiIiotp5HYD379+vuPnmm/VlZWVCSUkJwsPD5YsXLwp6vR6tW7eWGYD9A6c+JiIiIqqd10Mg5s6dq50wYYIjPz+/SKfTYe/evSVZWVnF/fv3F5ctW8buRD8xuFMEYox1h1sBzgkwOPUxERERBRuvA/DRo0eV8+bNsymVSiiVSpSXlwsdOnSQX3rppfL58+drmqKR5L0tGTmwOsRa13HqYyIiIgpmXgdglUolKxTO3Vq3bi39/vvvAgCYzWb57NmzXh+PGp+r/FlBqb3W9Wa9miXQiIiIKGh5PQa4b9++UlpamuKaa66RbrjhBnHBggWaCxcu2NauXRsSHx9fe5cjNRtPyp9pVAqMjo9ptjYRERER+ROve2xfeOEFa2xsrHz5frnZbMZDDz2ku3DhgvCvf/2LY4B9zF35MwDIsZSz/BkREREFLa96gCVJQnR0tNynTx8JAGJiYuQtW7aUNk3TqCFY/oyIiIiofl71AMuyjO7du4edOnWKY339FMufEREREdXPqyCrVCrRpUsX6cKFCywd4KdY/oyIiIiofl735L744ovlf/3rXzVHjx5lL7AfYvkzIiIiovp5XQXinnvu0ZWWlqJ///6hISEh0Ol0Vdbn5+cXNVrryCuu8md1VYAw69V4cXJvlj8jIiKioOZ1AP7b3/5mddUBJv/B8mdEREREnvE6AM+cObP22RXIp7wpfza0S2QztYqIiIjI/3jdlatUKg05OTk1BpBeuHBBUCqVhsZpFnmL5c+IiIiIPON1AJbl2v/IbrVaERISctUNooZh+TMiIiIiz3g8BOIf//hHCAAIgoA333xTHRYWVrFOFEXs2rVL2b17d6kJ2kgecJU/y7HU3sMrAIhh+TMiIiIizwPwP//5zxDA2QP81ltvhSiVyop1ISEhclxcnLxy5Ur+fd1HWP6MiIiIyDMeB+CTJ08WA8Af/vAH/WeffVYaEcGeRH/B8mdEREREnvO6CsS3335b2hQNoYZh+TMiIiIi73gdgB0OB9asWaPevn27Ki8vT6h+Udw333zDgNyMWP6MiIiIyDteB+CUlBTt2rVr1UlJSY6EhARREDim1JdY/oyIiIjIO14H4I8++kj1/vvvl02cONHRFA0i77D8GREREZF3vK4DHBISgm7durHcmZ8Y3CkCsSYt6uqHFwDEsvwZERERUQWvA/Cjjz5qe/XVV0MkiRnYHygVAp6ZEF/rRXAsf0ZERERUk9dDIL777jvlzp07VZs3b1b17NlTVKvVVdZ//vnnZY3WOnJr0/FsPP9lRq3rYkxaLJgYz/JnRERERJV4HYDNZrM8adIke1M0hrzjrv7vMxN6MvwSERERVeN1AH7nnXdYTqCJFVsdmPPhIZy6VIa4cB1emdofYdqqp8pd/V8BwPNfZmJsQiyHPxARERFV4nUABgC73Y7t27crf/31V8Xdd99tNxqNOHPmjGAymWSDwdDYbQwqk1bswtEzlorHP+UUIWHhZvRpZ8T6h26oWO6u/q8MILvQyvq/RERERNV4fRFcVlaWkJCQEDp58mT9I488os3LyxMAYOnSpSFz585lra2rUD38Vnb0jAWTVuyqeMz6v0REREQN43UAfvjhh7XXXnutmJ+fX6TT6SqW/7//9/8cO3bsUDZq64JIsdVRZ/h1OXrGgmKrs/zyyQslHh2X9X+JiIiIqvI6AH/33XfKZ555xqbRaKos79Spk3Tu3Dmvj0dOj7x/wOPtREnG+2mn3G7L+r9ERERENXkdWGVZFkRRrLH89OnTirCwsLquyarV888/H3LttdeGGgwGQ+vWrcMmTpyoy8zMrNKmsrIyzJo1SxsREREWFhZmuPXWW3XZ2dlVruo6efKkkJSUpNPr9YbWrVuHzZkzR2O3Vy1UsW3bNmW/fv1CNRqNoUuXLmGrV6+uWr/Nx9J/v+TxdmlZ+cixlLvd9o5BcbwAjoiIiKgarwPwyJEjHa+88kqI67EgCCgqKsLChQs1SUlJXk2PvHPnTlVycrJtz549JZs3by612+0YO3asvri4uGKbRx55RPvll1+qPvzww7Lt27eXZGdnC5MnT64Ye+FwODBhwgS9zWYTdu3aVfL222+XrV27Vj1//vyKLuoTJ04IkyZN0t94442OgwcPlqSkpJTPmjVLu3HjRr8ZsmFzeDaxiM0heTyut2Mr/dU0iYiIiKhFEmRZrn/gaTWnTp0Sxo4dq5dlGSdOnFAMGDBA/PXXXxWRkZHyzp07S2NiYrzqBa4sNzdXiImJCdu+fXvpiBEjxIKCAkRFRRnWrl1bNnXqVAcAZGRkKHr16hW6e/fu0sTERHHDhg3KW265RX/mzJni2NhYGQBWrFihnj9/vjYvL69Io9Hgscce03z11VeqjIyMioGzU6ZM0RUUFAhbtmwpddeuwsJCmM1mQ2FhIYxGY0NfXr0GPLcZ+WXuvz9E6FT4c2InvLL1F7fbvn//dawAQUREREHBYrHAZDKhoKCgyGQy1but1z3AcXFx8tGjR0uefvrp8pSUFFu/fv2kJUuWlB8+fLjkasIv4AyaABAZGSkDQHp6utJut2PMmDEVyTA+Pl5q3769vGfPHiUA7NmzR9WrVy/JFX4BYNy4cQ6LxYJjx44pAOD7779Xjhw5skq6HDNmjCMtLa3WHmCr1YrCwsKKm8Xi1XeEJiXLHP9LREREdDUaVAdYrVZj+vTpjunTp3s15KE+oijikUce0Q4dOlTs06ePBAA5OTlCSEgIwsPDq2wbFRUl5+TkCICz1zgqKqpK8HYFcXfbWCwWlJaWQq+vOlRg8eLFmiVLloSgGZWLng2BKHNIuGTl+F8iIiKihvK6B/j5558PefPNN2tcQPbmm2+qryY0JicnazMyMpQffPBBWUOP0VhSU1PLCwoKily3U6dOFTX1c6o8PBOCh5mW43+JiIiIaud1AF69enVIz549a3RXJiQkSG+99VaDAnBycrJ248aNqu3bt5fExcVV9NTGxMTINpsNly5VrZCQl5cnuHp5o6OjZddkHC6unl932xiNxhq9vwCg1WphMpkqbk017rcyjcqz6/EEeJaAWf+XiIiIqHZeB+Dc3FyhTZs2NQJwVFSU5AqenpIkCcnJydrPP/9ctW3bttIuXbpUGaYwaNAgUa1WY8uWLRVDNTIzMxWnT58Whg0bJgLAsGHDHD/88IOi8nNv3rxZZTQakZCQIAHAkCFDxO3bt1cZ7rF161bV4MGDa9Zz8xFZ9mz4dJnd/VAJjv8lIiIiqpvXAbhdu3bS7t27a4wd3r17t6ryhWieSE5O1r7//vvqd999t8xgMMjnzp0Tzp07J5SWOgszmM1mzJgxwz5v3jzt1q1blWlpaYp77rlHO2TIEDExMVEEgKSkJLFHjx7StGnTdAcPHlRs3LhRuWDBAs2DDz5o02qdvaCzZ8+2nTx5UjF37lxNRkaG4p///Kf6k08+Uc2ZM8f9YNpmUi569tZ5shXH/xIRERHVzeuL4O6991773LlzNTabDaNGjXIAwJYtW1RPPfWU5tFHH7V5cyzXWOKRI0dWGYfw1ltvWWfOnGkHgOXLl1vnzJmjvf322/Xl5eUYNWqUY+XKlRWFcFUqFTZs2FA6a9Ys7fXXXx+q1+vlu+++275kyZKKcNulSxd5/fr1pXPnztW+/vrrIW3btpVXrVplHT9+vN/0AGuUjRdYOf6XiIiIqG5e1wGWJAmPP/645o033gix2Zx5V6vV4rHHHitftGiRVwE4UDRHHeAhi79GbrHd/YYeYP1fIiIiCjbe1AH2ugdYoVDg73//e/nChQvLf/jhB4Ver0f37t0l13ADahiN2uvRKLXi+F8iIiKi+jWoDjAAGAwGXHfddZ4VryW3FELjBGCO/yUiIiKqn9cBuLi4GEuWLNHs2LFDef78eYUkVc3AWVlZxY3WuiASEarGyfyrL4HM8b9ERERE9WvIRXC6Xbt2Kf/0pz/ZY2NjHYKnMzNQvdqG63Hw9NVPucz6v0RERET18zoAf/3116r169eXDh8+3G8qKLQEBm2NyfW8xvG/RERERO55PfDUbDbLkZGRXtX7Jfcaoyed43+JiIiI3PM6AD/33HPlzzzzjKakpKQp2hPErv47Bcf/EhEREbnn9RCIf/zjHyFZWVmKmJgYQ1xcnKRWV/3T/eHDh5mMG8CgufohEBz/S0REROSe1wF40qRJjTNbA1WRU3h1FSAiQ0M4/peIiIjIA14H4Oeff75Fzvbme1c3dveWfm04/peIiIjIAw2eCCMtLU2RmZmpBIBevXqJAwcO5KQYVyHGdHXDF0bHxzRSS4iIiIhaNq8DcE5OjnDHHXfodu7cqTSbzQCAgoIC/OEPfxA/+OCDsujoaFaIaIDickeD92X5MyIiIiLPeV0F4qGHHtIWFRUJx44dK8nPzy/Kz88vOnr0aInFYhFSUlJ4FVYDiXLDvzew/BkRERGR57zuAd6yZYtq8+bNJb169aoY8pCQkCCtWLGibNy4caGN27zgcb6ovMH7svwZERERkee87gGWJAnVS58BgFqthiRxGHBDSXLD3zuWPyMiIiLynNcB+A9/+IPj0Ucf1Z45c6bib+6nT58W5s6dq73xxhsbPpA1yOVZGlZcQyEA13YIb+TWEBEREbVcXgfg119/3WqxWITOnTuHuW5dunQJs1gswooVK6xN0chgEG3SNGg/SQYO/H6pkVtDRERE1HJ5PQa4Q4cO8qFDh0q+/vprZWZmpgIA4uPjpbFjx4qN37zgEW1oWAAGgLwifu8gIiIi8lSD6gArFAokJSWJSUlJDL2NxKgNafC+HANMRERE5DmPh0Bs2bJF2aNHj9DCwsIa6woKCtCzZ8/Qb775RtmorQsi2Q2cCtmsV7MGMBEREZEXPA7Ar776ash9991nN5lMNdaZzWbcf//99n/84x8N78YMcueLG1YGbcbQjqwBTEREROQFjwPwsWPHlOPHj6+zykNSUpLj0KFD7AFuIKutYaNJBnVk7y8RERGRNzwOwHl5eYJara5zujKVSiVfuHCBXZENZLU3LABfKGn4BBpEREREwcjjANymTRv52LFjdfbwHjlyRBkTE9Pw+XyDXEGpvUH78QI4IiIiIu94HICTkpIczz77rKasrObFWqWlpVi4cKFm/PjxDUtxhNIGDIEQAF4AR0REROQlj8ugPfPMM+X/+9//Qrt37x6WnJxs69GjhwQAmZmZilWrVoWIoojU1NSGTWdGKLF5P4meSiHwAjgiIiIiL3kcgGNjY+XvvvuuZNasWdpnnnlGI8vO0Q6CIGDUqFGON954wxobG8shEA1kl7zfp3WYuvEbQkRERNTCeTURRqdOneTNmzeX5efn4+eff1bIsoxrrrlGiojgn+F94S83dfV1E4iIiIgCToNmgouIiMB1113XgD5LakydWxt93QQiIiKigOPxRXDUtG7p3car7c06zgBHRERE1BAMwH6ibaTOq+17tTHyAjgiIiKiBmAA9hMmnXcXtJ28UNJELSEiIiJq2RiA/URBmXcV5IrLvS+bRkREREQMwH4ju8Dq1fZtzZwBjoiIiKghGID9RKyXgfbx0T2aqCVERERELRsDsJ8w60K82n54z6gmagkRERFRy8YA7CcsVrvH26oEsAIEERERUQMxAPsJ2YtJpEVOOE1ERETUYAzAfkJQeD6xHvMvERERUcMxAPuJ//vulK+bQERERBQUGID9hNUu+roJREREREGBAdhPmL2YCe7vt/ZqwpYQERERtWwMwH5iQ8pwj7e9dXCHJmwJERERUcvGAOwnYsxa6NTuT8eqaQNYAo2IiIjoKjAA+5HM58fVG4JXTRuApITYZmwRERERUcuj8nUDqKrM58chp8CKCf/8FoVWB3RqJZZO7ouk3jHs+SUiIiJqBAzAfijGrMWBZ8f6uhlERERELRKHQBARERFRUGEAJiIiIqKgwgBMREREREGFAZiIiIiIggoDMBEREREFFQZgIiIiIgoqDMBEREREFFQYgImIiIgoqPg0AO/YsUM5fvx4XWxsbJggCIZPPvmkysQckiTh6aef1sTExITpdDrDiBEj9D/99FOVNl+8eBF33HGHzmg0Gsxms2HGjBnaoqKiKs9z+PBhRWJiol6r1RratWsX9sILL4Q0w8sjIiIiIj/k0wBcUlKCPn36SK+99pq1tvUvvvhiyBtvvBHyxhtvWPfu3VsSGhoqJyUl6cvKyiq2ufPOO/UZGRmKTZs2lX7++eelu3fvVs6cOVPnWl9YWIixY8fq4+LipPT09JKXXnrJunjxYs0bb7yhboaXSERERER+RpBl2eLrRgCAIAiGjz/+uOy2225zAM7e3zZt2oQ9+uijtieffNIGAAUFBYiJiTGsWbOm7K677nL88MMPioSEhNB9+/aVDBkyRAKAL7/8Ujlx4kT9qVOnitu1aye/9tpr6gULFmizs7OLNBoNAGDevHma9evXq37++ecST9pWWFgIs9lsKCwshNFobKJ3gIiIiIgaymKxwGQyoaCgoMhkMtW7rd+OAf7tt9+E3NxcYfTo0Q7XMrPZjEGDBol79+5VAsB3332nNJvNcIVfABgzZoyoUCiwb98+JQDs27dPmZiY6HCFXwBISkpy/PLLL4r8/Pxan9tqtaKwsLDiZrH4xXcEIiIiImoEfhuAs7OzFQAQExMjV14eFRUl5+TkKAAgJydHaN26tVR5vVqtRnh4uJydnS1c3kYRHR1d5RiuY7qeo7rFixdrzGazwXWLi4szNN4rIyIiIiJf8tsA7EupqanlBQUFRa7bqVOnitzvRURERESBQOV+E9+IjY2VAGcvb9u2bSt6cPPy8oS+ffuKgLMn9/z581VCvN1ux6VLl4TY2Fj58jZSbm6uUHmbnJwcofJzVKfVaqHVahv3BRERERGRX/DbHuDOnTvL0dHR8tatWytCemFhIdLT05VDhw4VASAxMVEsKChAWlpaxevYunWrUpIkXHfddSIAXHfddeJ3332nstlsFcf++uuvVd26dZMiIiKa8RURERERkT/waQAuKirCgQMHFAcOHFAAwG+//aY4cOCA4uTJk4JCoUBKSopt6dKlms8++0x15MgRxbRp03SxsbHy5MmTHQDQq1cvafTo0eIDDzyg27t3r2Lnzp3Khx9+WDtlyhRHu3btZAC4++677Wq1Wr7nnnu0x44dU7z33nuq119/PeTRRx+11dc2IiIiImqZfFoGbdu2bcpRo0bpqy+fNm2afe3atVZJkpCamqpZs2aNurCwUBg6dKi4cuVKa48ePSqGLly8eBGzZ8/Wbdy4UaVQKHDrrbfaV6xYYTUYrly3dvjwYcVf/vIX7YEDB5SRkZHy7NmzbfPnz/c4ALMMGhEREZF/86YMmt/UAfZnDMBERERE/q1F1AEmIiIiImoKDMBEREREFFQYgImIiIgoqDAAExEREVFQYQAmIiIioqDCAExEREREQYUBmIiIiIiCCgMwEREREQUVBmAiIiIiCioMwEREREQUVBiAiYiIiCioMAATERERUVBhACYiIiKioMIATERERERBhQGYiIiIiIIKAzARERERBRUGYCIiIiIKKgzARERERBRUGICJiIiIKKgwABMRERFRUGEAJiIiIqKgwgBMREREREGFAZiIiIiIggoDMBEREREFFQZgIiIiIgoqDMBEREREFFQYgImIiIgoqKh83QCqRf5Z4M1hQHkxoAkDHtgDRLT1dauIiIiIWgQGYH+zqDUg2a48thYA/4wHFCHAs+d91iwiIiKiloJDIPxJ9fBbmWRzriciIiKiq8IA7C/yz9Ydfl0km3M7IiIiImowBmB/8fqgxt2OiIiIiGrFAOwvxJLG3Y6IiIiIasUATERERERBhQGYiIiIiIIKy6ARETU1SQR+3wMU5wJh0UCHYYBC6b/HbQ5su2+w7S2bP71H/tSWWjAAEwW7QAxngdTmjPXApicAy7kry4xtgKSXgPhJ/nfc5sC2+wbb3rL503vkT22pgyDLssXXjfB3hYWFMJvNhsLCQhiNxqZ5koUmL7YtbJo2kPeuNjA1RuC6mmMEYjgLpDZnrAc+mg5ArrZCcP5z+zsNO3ZTHbc5sO2+wba3bP70HvmwLRaLBSaTCQUFBUUmU/25igHYAwzATcCT0OZum6td7+k2dbnawNQYgetqjhGI4SyQ2iyJwKsJVc9N9WMb2wCPHvP+S1NTHLc5BGLbZdl5k0Tgn33ctD0W+Mv+y22/vJ/zIFeO5Xpc67rq26GedfXtV22dJAJrRjt/z9XV9rBo4J6vAEUdlwZVb9OVFV5uX9dh6thecgDvTASK8+reNywauPvzK++7V+1pAdtLIvD+HUBJPbPFhrYG7ni/nvNb145etkdyAB9NA0ou1HG8pv0ZZwBuZH4XgDsMB8KiKj6bAQCCAjC1BfStgNBWQOlFQB9Z9d/Q1oAhFmg/BDj9vfOXob4VIAjOH5yw6PrXucJhXaHR0+WlF4HNT9Uf2twFu6td78lz1OdqA1NjBK6rOYY/hjNXyJBF5y/RivuXb6INWD2yng9xOP+PT33P+RbI0pWbJFZ9XPnmsANfPgqUXar7uFozcFPq5XbWdUzxcli6/Dg/Czj2kfv37JrxgCHmSliRpUr35ZrLi3OBrG/dH7fdIEAXUcsx3Rzf7X1U2lfy4H6l57KXAWUX3bc9JAxQqquFO9fKhgTLBqwjoqbz5w1Apxsa/bAMwI3M7wLw1RIUlz+gvFxnbAMk/BE4/nHN0OjN8tqf2PnP7e84/60v2A1LAfa81vD1njxHU4ZHT/d/aP/loGUHxMs3yQ6IDsBhBdbeWv83fl0EMGaxM5iJNud+rmNdPAEcfrfufV06Dgf0EVWDaJWAKlUNq2UFwKUs98dVhzq/XFXet67/d0RUh8u/rwThyuPK913rJAmQHe4PpwwBFCpU7V2p/HR1LG+07WtZJtqcX5zcUYcCqpBGbk+AbG8vA8ry6zhWJboIICS0nuPXtaMX7bEV19P7W8lta4Def3S/nZcYgBtZiwvAfktw9lALqD8w1xfSPVmvbw0Icv0/pLpwYNTCy6HPcTmA2pz3L54Ajn7g7sUAbQcCGkO1/e1AWSFQeMr9/lSJcPm8iu431Uc633dB6dzHdVMonb+wK5ZdXl+aD+T/6v64bQYA5vZujltpXVEO8OMX7o/b904gvOOV1yi4Xu/lx9XvX8oC0le7P+7QFKD1NZc/pFzHrn4fdSyvfr9yW1B7u+q9f/k42YeBDXPct/2WN4B2A1FrwKvyuNq6Wrer7xgeBkgAOPU98OGf3Lf9zo+ADkO9P76n7a8zNNUjaxfwfze7366JeuWuSiC3vbn403vk47Z4E4BZBYL8iAwUuesphvueQnfrS+vpNXUpuwR88Yj77epzdv/V7V+dQgUo1M4/DUsiYPdgVsCoeMAc59xXqb6yf8kF4JfN7vcfdD/QqtvlkKe83AbXfWXN5XmZwNYF7o9767+AuMHO/SqOpbocKFW1PJ/g+S/WKf/n3S9WT487epF3x63o6c9G7X9Wv9zTf8vr3g8z+Wmj++OOfs5/xtG6xPYFdv7Nfdv73uF/bb8mydk2d23vNsr/2t5hmGdt7zCsuVvmXiC3vbn403vkT21xgwGYqC4xfZzhsXJwVKicww5+2uh+/2EPA9EJgLJScFWogfOZwNep7ve/8wOg0/Arf5as3PPjaWgbt6z20OZpOBv3kncf5l1HAWn/cn/cPlO8DwlN9Yu1qY6rUDrHk3803XmMKse+fC6Tlnr/PjTVcZsD2+4bbHvL5k/vkT+1xQ3OBOcv1KG+bgFVN/YF4I51wJT/ALe9Bdz6BjDpn8DUd52BqM4BUwJgbOscQtF3KpBwm3M88TXjnL1D1832bP9uY5zjtZTqmn/2dIU2d8eoK7S5fkm5tq2+L3B14ayxj9uUx27KNsdPco4nN8ZWXW5sc3XVMJrquM2BbfcNtr1l86f3yJ/aUg+OAfZAs4wBvnAKWNG7aY4dMCqPAa6rNw7OP5NXvjrcq/WePIfgvgJCRQUGoNZvuB5XgWjg/o15jBqVMNo6A1+j19RthOM25bGbss2BNHFHc2HbfYNtb9n86T3yQVt4EVwja5YADAT5hXCVQhtQf7CrqPLQwPWePEdzhMfGCFyNcYxADGeB2GYiImpSDMCNrNkCMBC8Ibh6aHMX7K52vSfP4YlAnwmOiIiohWAAbmTNGoCBpg/BDa4D3NY5nrVGvd8GLB/zAhAaGdgzwREREZHfYABuZM0egAHg3E/Am4OrLRSA8C7O8amBPBMcQyYRERE1MgbgRuaTAExEREREHvMmALMMGhEREREFFQZgIiIiIgoqDMBEREREFFQYgImIiIgoqARVAF6+fLm6Q4cOYVqt1jBo0CD93r17g+r1ExEREVEQBeD33ntP9de//lWbmppavn///pI+ffpI48ePD83JyRHc701ERERELUXQBOBXXnkl5N5777Xff//99oSEBOnNN9+06nQ6efXq1Wpft42IiIiImk9QBODy8nIcOnRIOWrUKIdrmVKpxE033eTYt29fjRkZrFYrCgsLK24WC0slExEREbUUKl83oDmcP39eEEUR0dHRcuXlUVFR8k8//VTjS8DixYs1S5YsCam+nEGYiIiIyD+5cposy262DJIA7K3U1NTyxx9/vNz1+MyZM0hISDC0b9/el80iIiIiIjeKiopgNpvr3SYoAnDr1q1lpVKJ3NzcKhe85eXlCdV7hQFAq9VCq9VWPA4NDcWpU6eKwsLCoFA0z6gRi8WCuLg4w6lTp4o4/XLg4fkLfDyHgY/nMLDx/AW+5j6HkiShuLgYsbGxbrcNigCs0WjQv39/cdu2barbbrvNAQCiKGLHjh2q5ORkm7v9VSoVfNX7azQa4W4+a/JfPH+Bj+cw8PEcBjaev8DXnOcwPDzco+2CIgADwJw5c2z33nuvbuDAgeKQIUPEV155JaS0tFS477777L5uGxERERE1n6AJwH/6058ceXl51oULF2pyc3OFPn36iF9++WVpbGys+5HSRERERNRiBE0ABoBHH33U/uijjwZEj69Go8H8+fNtGo3G102hBuD5C3w8h4GP5zCw8fwFPn8+h4Isy6ztRURERERBIygmwiAiIiIicmEAJiIiIqKgwgBMREREREGFAZiIiIiIggoDsB9avny5ukOHDmFardYwaNAg/d69e3me/NSOHTuU48eP18XGxoYJgmD45JNPqlRWkSQJTz/9tCYmJiZMp9MZRowYof/pp594Pv3E888/H3LttdeGGgwGQ+vWrcMmTpyoy8zMrHJ+ysrKMGvWLG1ERERYWFiY4dZbb9VlZ2cLdR2Tmtdrr72mTkhICDUajQaj0WgYMmSIfsOGDUrXep6/wLJ48eIQQRAMKSkpFWUDeA79X2pqqkYQBEPlW/fu3UNd6/3xHPKD2M+89957qr/+9a/a1NTU8v3795f06dNHGj9+fGhOTg5/2P1QSUkJ+vTpI7322mvW2ta/+OKLIW+88UbIG2+8Yd27d29JaGionJSUpC8rK2vuplItdu7cqUpOTrbt2bOnZPPmzaV2ux1jx47VFxcXV2zzyCOPaL/88kvVhx9+WLZ9+/aS7OxsYfLkyTofNpsqad++vfziiy+Wp6enl6SlpZXceOON4uTJk/XHjh1TADx/gWTfvn2K1atXhyQkJEiVl/McBoaePXtKZ8+eLXbdvvvuu1LXOn88hyyD5mcGDRqkHzhwoLRy5Uor4JyyuX379mGzZ8+2paamup22mXxHEATDxx9/XOaabluSJLRp0ybs0UcftT355JM2ACgoKEBMTIxhzZo1ZXfddZfDty2m6nJzc4WYmJiw7du3l44YMUIsKChAVFSUYe3atWVTp051AEBGRoaiV69eobt37y5NTEwUfd1mqikiIsLw4osvWqdOnWrn+QsMRUVFGDBgQOiKFSusS5Ys0fTt21d87bXXyvkzGBhSU1M169evVx09erSk+jp/PYfsAfYj5eXlOHTokHLUqFEVwUipVOKmm25y7Nu3T1nfvuR/fvvtNyE3N1cYPXp0xfk0m80YNGiQuHfvXp5PP1RYWAgAiIyMlAEgPT1dabfbMWbMmIpzGB8fL7Vv317es2cPz6GfcTgcWLdunaqkpASJiYkiz1/gSE5O1o4bN84xduzYKmGI5zBwnDhxQhEbGxvWqVOnsDvuuEN38uRJAfDfcxhUM8H5u/PnzwuiKCI6OrrK9MxRUVEyx40GnuzsbAUAxMTE1DifOTk5PJ9+RhRFPPLII9qhQ4eKffr0kQAgJydHCAkJQXh4eJVtL59DDkvyE0eOHFEkJiaGWq1WhIWF4eOPPy5LSEiQDh06pOL583/r1q1THTp0SLl///4avYf8GQwM1113nWPNmjVijx49pHPnzgmLFi3SDB8+PPT48ePF/noOGYCJiODsgcrIyFDu2rWrxocw+bcePXpIBw8eLC4oKBD++9//qu+55x7tN998U+p+T/K133//XZgzZ47266+/LtXpOKw3UN18880VPff9+vXD0KFDSzt27Gh4//331Xq9Xq5vX19hL5Qfad26taxUKpGbm1vlG1FeXp5QvVeY/F9sbGxFL2Ll5Xl5eUJMTIxU+17kC8nJydqNGzeqtm/fXhIXF1fxsxYTEyPbbDZcunSpyvaXzyF/Jv2ERqNB9+7d5cGDB0t/+9vfynv37i29+uqrITx//m///v3K8+fPCwMHDgxVqVQGlUpl2LVrl/L1118PUalUhujoaJ7DABQeHo6uXbtKv/76q8Jffw4ZgP2IRqNB//79xW3btlX0zIuiiB07dqiuu+46DvQPMJ07d5ajo6PlrVu3VpzPwsJCpKenK4cOHcrz6QckSUJycrL2888/V23btq20S5cuVX4ZDxo0SFSr1diyZUvFOczMzFScPn1aGDZsGM+hn5JlGeXl5Tx/AWD06NGOI0eOlBw8eLDiNmDAAOmOO+6wHzx4sGTw4ME8hwGoqKgIWVlZitjYWNlffw45BMLPzJkzx3bvvffqBg4cKA4ZMkR85ZVXQkpLS4X77rvP7uu2UU1FRUX4+eefK75I/vbbb4oDBw4oIiMj5Y4dO8opKSm2pUuXarp37y517txZSk1N1cTGxsqTJ09mBQg/kJycrP3www/Vn376aanBYJDPnTsnAIDZbJb1ej3MZjNmzJhhnzdvnjYiIqLMaDTKDz/8sHbIkCEirz73D48//rhm/Pjxjg4dOkhFRUXCu+++q965c6dy48aN5Tx//s9oNMI15t4lNDRUjoyMlF3LeQ7935w5czQTJ050dOzYUTp79qxiwYIFGoVCId911112f/05ZBk0P/Tqq6+q//GPf2hyc3OFPn36iMuXLy/nN13/tG3bNuWoUaP01ZdPmzbNvnbtWqskSUhNTdWsWbNGXVhYKAwdOlRcuXKltUePHhwC4QcEQTDUtvytt96yzpw50w44C7jPmTNH+9FHH6nLy8sxatQox8qVK61t2rThn1/9wIwZM7Q7duxQ5eTkCEajUU5ISJCeeOKJ8qSkJBHg+QtEw4cP17vKoAE8h4FgypQput27dyvz8/OFVq1aycOGDRNfeOEFa7du3WTAP88hAzARERERBRWOASYiIiKioMIATERERERBhQGYiIiIiIIKAzARERERBRUGYCIiIiIKKgzARERERBRUGICJiIiIKKgwABMRERFRUGEAJiIKEHfffbd24sSJuuZ+3tWrV6sFQTAIgmBISUnR1Ldthw4dwl5++eWQyo9d+166dKnpG0tE5AGVrxtARER1T8vsMn/+fNtrr71mlWXfzBxqNBqRmZlZHBYW5lUD0tLSSnbu3Km8/fbbmz24ExHVhQGYiMgPnD17tth1//3331cvWrRIk5mZWbHMYDDIBkO9GblJCYKANm3aeJ2+o6Oj5YiICN+kdiKiOnAIBBGRH2jTpo3suplMJtkVOF03g8FQYwjE8OHD9bNnz9ampKRowsPDDVFRUWErV65UFxcXY/r06VqDwWDo0qVL2IYNG5SVn+vo0aOKMWPG6MPCwgxRUVFhf/rTn7Tnz58XvG1zTk6OMH78eJ1OpzN07Ngx7J133mGnChEFBAZgIqIA9u6776ojIyPlffv2lcyePduWkpKive2223RDhw4V9+/fXzJq1CjHn//8Z11JSQkA4NKlSxg5cqS+X79+YlpaWsnGjRtLc3NzFVOmTPF6iMKf//xn7ZkzZxRbt24t/eijj0pXrlwZ0pAgTUTU3BiAiYgCWO/evcWFCxfarrnmGik1NdWm1WrRqlUrOTk52X7NNddICxYsKM/PzxcOHz6sBIDly5eH9O3bV1q2bFl5fHy8NHDgQOntt98u+/bbb5U//vijx58JP/74o+Lrr79Wvfnmm2WJiYni4MGDpTVr1ljLysqa7sUSETUS/rmKiCiA9e7dW3LdV6lUiIiIkBMSEiqWxcTEyACQl5cnAMDRo0eVO3fuVIaFhdUYUPzrr78KPXr08Oh5MzIyFCqVCoMGDap4rvj4eMlsNjf8xRARNRMGYCKiAKZWq6tcYCYIAtRqdcVjhcLZqStJzpxaXFwsjB8/3rFs2TJr9WM15CI3IqJAxABMRBRE+vfvL3722WeqTp06yZWDsrd69uwpORwOpKenK6677joJADIzMxUFBQWN1VQioibDMcBEREEkJSXFdunSJWHq1Km6ffv2KX755Rdh48aNyunTp2sdDofHx+nZs6c0evRocdasWbo9e/Yo09LSFDNnztTqdCz3S0T+jwGYiCiItGvXTt69e3epKIoYN25caN++fcPmzJmjNZvNsmu4hKf+85//lMXGxko33XST/o9//KP+/vvvt7du3ZrDKIjI7wmyLFt83QgiIvJfq1evVs+bN09bUFBQ1JD9t23bphw1apQ+Pz+/KDw8vLGbR0TkNfYAExGRW4WFhQgLCzM89thjGm/269mzZ+jNN9+sb6p2ERE1BHuAiYioXhaLBTk5OQIAhIeHw5thDllZWYLdbgcAdOnSRVYqlW72ICJqegzARERERBRUOASCiIiIiIIKAzARERERBRUGYCIiIiIKKgzARERERBRUGICJiIiIKKgwABMRERFRUGEAJiIiIqKgwgBMREREREHl/wNlwkTQtiNMWQAAAABJRU5ErkJggg==\n", + "image/png": 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\n", 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" ] @@ -748,27 +910,55 @@ { "cell_type": "markdown", "id": "0eb92bf1", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "source": [ "So far we've learned how to simulate any dynamic system developed with QSDsan. \n", - "A complete list of existing unit operations within QSDsan is available [here](https://qsdsan.readthedocs.io/en/latest/api/sanunits/_index.html). The column \"Dynamic\" indicates whether the unit is enabled for dynamic simulations. Any system composed of the enabled units can be simulated dynamically as we learned above.\n", - "\n", + "A complete list of existing unit operations within QSDsan is available [here](https://qsdsan.readthedocs.io/en/latest/api/sanunits/_index.html). The column \"Dynamic\" indicates whether the unit is enabled for dynamic simulations. Any system composed of the enabled units can be simulated dynamically as we learned above." + ] + }, + { + "cell_type": "markdown", + "id": "497d72b8", + "metadata": { + "slideshow": { + "slide_type": "skip" + } + }, + "source": [ "[Back to top](#top)" ] }, { "cell_type": "markdown", "id": "3d13e036", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### 1.2. When is a system \"dynamic\"?\n", + "It's ultimately the user's decision whether a system should be run dynamically. This section will cover the essentials to switch to the dynamic mode for system simulation." + ] + }, + { + "cell_type": "markdown", + "id": "94eab6a5", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "source": [ - "### 1.2. What makes a system \"dynamic\"?\n", - "It's ultimately the user's decision whether a system should be run dynamically. This section will cover the essentials to switch to the dynamic mode for system simulation.\n", - "\n", "#### `System.isdynamic` vs. `SanUnit.isdynamic` vs. `SanUnit.hasode` \n", "\n", "- Simply speaking, when the `.isdynamic == True`, the program will attempt dynamic simulation. Users can directly enable/disable the dynamic mode by setting the `isdynamic` property of a `System` object.\n", "\n", - "- The program will deduct the value of `.isdynamic` when it's not specified by users. `.isdynamic` is considered `True` in all cases except when `.isdynamic == False` for all units.\n", + "- The program will set the value of `.isdynamic` when it's not specified by users. `.isdynamic` is considered `True` in all cases except when `.isdynamic == False` for all units.\n", "\n", "- Setting `.isdynamic = True` does not gaurantee the unit can be simulated dynamically. Just like how the `_run` method must be defined for static simulation, a series of additional methods must be defined to enable dynamic simulation.\n", "\n", @@ -777,9 +967,13 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 17, "id": "c130f36f", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "outputs": [ { "data": { @@ -792,7 +986,7 @@ " : True}" ] }, - "execution_count": 15, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -802,407 +996,14 @@ "{u: u.hasode for u in sys.units}" ] }, - { - "cell_type": "code", - "execution_count": 16, - "id": "b6a0612a", - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "M1Mixer:e->A1CSTR:c\n", - "\n", - "\n", - "\n", - " ws26\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "A1CSTR:c->A2CSTR:c\n", - "\n", - "\n", - "\n", - " ws11\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "A2CSTR:c->O1CSTR:c\n", - "\n", - "\n", - "\n", - " ws13\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "O1CSTR:c->O2CSTR:c\n", - "\n", - "\n", - "\n", - " ws15\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "O2CSTR:c->O3CSTR:c\n", - "\n", - "\n", - "\n", - " ws17\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "O3CSTR:c->M1Mixer:c\n", - "\n", - "\n", - "\n", - " RWW\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "O3CSTR:c->C1Flat bottom circular clarifier:c\n", - "\n", - "\n", - "\n", - " treated\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "C1Flat bottom circular clarifier:c->M1Mixer:c\n", - "\n", - "\n", - "\n", - " RAS\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "C1Flat bottom circular clarifier:c->J1ASMto ADM:c\n", - "\n", - "\n", - "\n", - " WAS\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "C1Flat bottom circular clarifier:c-> effluent:w\n", - "\n", - "\n", - " effluent\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "J1ASMto ADM:c->AD1Anaerobic CSTR:c\n", - "\n", - "\n", - "\n", - " ws21\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "AD1Anaerobic CSTR:c->J2ADMto ASM:c\n", - "\n", - "\n", - "\n", - " ad eff\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "AD1Anaerobic CSTR:c-> biogas:w\n", - "\n", - "\n", - " biogas\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "J2ADMto ASM:c->M1Mixer:c\n", - "\n", - "\n", - "\n", - " ws25\n", - "\n", - "\n", - "\n", - "\n", - "\n", - " wastewater:e->M1Mixer:c\n", - "\n", - "\n", - " wastewater\n", - "\n", - "\n", - "\n", - "\n", - "\n", - " filler0:e->A1CSTR:c\n", - "\n", - "\n", - " filler0\n", - "\n", - "\n", - "\n", - "\n", - "\n", - " filler1:e->A1CSTR:c\n", - "\n", - "\n", - " filler1\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "M1Mixer\n", - "\n", - "\n", - "M1Mixer\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "A1CSTR\n", - "\n", - "\n", - "A1CSTR\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "A2CSTR\n", - "\n", - "\n", - "A2CSTR\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "O1CSTR\n", - "\n", - "\n", - "O1CSTR\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "O2CSTR\n", - "\n", - "\n", - "O2CSTR\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "O3CSTR\n", - "\n", - "\n", - "O3CSTR\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "C1Flat bottom circular clarifier\n", - "\n", - "\n", - "C1Flat bottom circular clarifier\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "J1ASMto ADM\n", - "\n", - "\n", - "J1ASMto ADM\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "AD1Anaerobic CSTR\n", - "\n", - "\n", - "AD1Anaerobic CSTR\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "J2ADMto ASM\n", - "\n", - "\n", - "J2ADMto ASM\n", - "\n", - "\n", - "\n", - "\n", - "\n", - " wastewater\n", - "\n", - "\n", - "\n", - "\n", - " effluent\n", - "\n", - "\n", - "\n", - "\n", - " biogas\n", - "\n", - "\n", - "\n", - "\n", - " filler0\n", - "\n", - "\n", - "\n", - "\n", - " filler1\n", - "\n", - "\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Units without ODEs can also be simulated dynamically as long as \n", - "# the fundamental methods are defined. Here is an example.\n", - "from exposan import interface as inter\n", - "inter.load()\n", - "inter.sys.diagram()" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "f2a81479", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{: False,\n", - " : True,\n", - " : True,\n", - " : True,\n", - " : True,\n", - " : True,\n", - " : True,\n", - " : False,\n", - " : True,\n", - " : False}" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "{u: u.hasode for u in inter.sys.units}\n", - "# inter.sys.isdynamic" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "be199e8d", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\joy_c\\Dropbox\\PhD\\Research\\QSD\\codes_developing\\QSDsan\\qsdsan\\sanunits\\_junction.py:573: UserWarning: Ignored dissolved H2 or CH4.\n", - " warn('Ignored dissolved H2 or CH4.')\n" - ] - }, - { - "data": { - "text/plain": [ - "(,\n", - " ,\n", - " ,\n", - " ,\n", - " )" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "uf = inter.sys.flowsheet.unit\n", - "inter.sys.simulate(t_span=(0,3), method='BDF', state_reset_hook='reset_cache')\n", - "inter.sys.scope.subjects" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "ff2ea29e", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = uf.AD1.scope.plot_time_series(('S_ch4_gas', 'S_h2_gas', 'S_IC_gas'))" - ] - }, { "cell_type": "markdown", "id": "7839f0e2", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "skip" + } + }, "source": [ "[Back to top](#top)" ] @@ -1210,7 +1011,11 @@ { "cell_type": "markdown", "id": "33a3d638", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "source": [ "## 2. Writing a dynamic `SanUnit` \n", "\n", @@ -1220,7 +1025,11 @@ { "cell_type": "markdown", "id": "220c984a", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "source": [ "### 2.1. Basic structure \n", "\n", @@ -1228,39 +1037,57 @@ "\n", "In comparison, during dynamic simulations, all information are stored as `_state` and `_dstate` attributes of the relevant `SanUnit` obejcts as well as `state` and `dstate` properties of `WasteStream` objects. These information won't be translated to mass or energy flows until dynamic simulation is completed.\n", "\n", - "- `WasteStream.state` is a 1d `numpy.array` of length $n+1$, $n$ is the length of the components associated with the `thermo`. Each element of the array represents value of one state variable.\n", + "- `WasteStream.state` is a 1d `numpy.array` of length $n+1$, $n$ is the length of the components associated with the `thermo`. Each element of the array represents value of one state variable." + ] + }, + { + "cell_type": "markdown", + "id": "b1529db3", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "---\n", + "#### Tip\n", "\n", - " ---\n", - " #### Tip\n", - " \n", - " Typically for a liquid `WasteStream`, the first $n$ element represents the component concentrations \\[mg/L\\], while the last element represents the total volumetric flow \\[m3/d\\]. For a gaseous `WasteStream`, the first $n$ state variables can simply be the mass flows \\[g/d\\] of the components if the last element is fixed at 1. This is because after completing dynamic simulations, the `WasteStream`'s mass flow is defined as the first $n$ element of this array multiplied by the last element.\n", + "Typically for a liquid `WasteStream`, the first $n$ element represents the component concentrations \\[mg/L\\], while the last element represents the total volumetric flow \\[m3/d\\]. For a gaseous `WasteStream`, the first $n$ state variables can simply be the mass flows \\[g/d\\] of the components if the last element is fixed at 1. This is because after completing dynamic simulations, the `WasteStream`'s mass flow is defined as the first $n$ element of this array multiplied by the last element.\n", "\n", - " ---" + "---" ] }, { "cell_type": "markdown", "id": "d997b05d", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ "- `WasteStrem.dstate` is an array of the exact same shape as `WasteStream.state`, storing values of the time derivatives (i.e., the rates of change) of the state variables." ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 18, "id": "a8ae235a", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "outputs": [ { "data": { "text/plain": [ "array([3.000e+01, 8.899e-01, 4.389e+00, 1.886e-01, 9.784e+00, 5.720e-01,\n", - " 1.722e+00, 4.898e-01, 1.038e+01, 1.747e+00, 6.884e-01, 1.349e-02,\n", - " 4.954e+01, 2.751e+01, 9.986e+05, 1.806e+04])" + " 1.722e+00, 4.897e-01, 1.038e+01, 1.747e+00, 6.884e-01, 1.349e-02,\n", + " 4.954e+01, 2.751e+01, 9.978e+05, 1.806e+04])" ] }, - "execution_count": 20, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -1272,19 +1099,23 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 19, "id": "ab1496fd", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "outputs": [ { "data": { "text/plain": [ "sparse([3.000e+01, 8.899e-01, 4.389e+00, 1.886e-01, 9.784e+00, 5.720e-01,\n", - " 1.722e+00, 4.898e-01, 1.038e+01, 1.747e+00, 6.884e-01, 1.349e-02,\n", - " 4.954e+01, 2.751e+01, 9.988e+05])" + " 1.722e+00, 4.897e-01, 1.038e+01, 1.747e+00, 6.884e-01, 1.349e-02,\n", + " 4.954e+01, 2.751e+01, 9.981e+05])" ] }, - "execution_count": 21, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -1296,9 +1127,13 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 20, "id": "825050c1", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "outputs": [ { "data": { @@ -1306,7 +1141,7 @@ "True" ] }, - "execution_count": 22, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -1318,8 +1153,13 @@ { "cell_type": "markdown", "id": "eb706d47", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "source": [ + "\n", "- `SanUnit._state` is also a 1d `numpy.array`, but the length of the array is not assumed, because the state variables relevant for a `SanUnit` is entirely dependent on the unit operation itself. Therefore, there is no predefined units of measure or order for state variables of a unit operation.\n", "\n", "- `SanUnit._dstate`, similarly, must have the exact same shape as the `_state` array, as each element corresponds to the time derivative of a state variable." @@ -1327,9 +1167,13 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 21, "id": "956dbc0f", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "outputs": [ { "data": { @@ -1337,43 +1181,48 @@ "False" ] }, - "execution_count": 23, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "C1._state.shape == A1._state.shape" + "C1._state.shape == A1._state.shape\n", + "# C1._state.shape == C1._dstate.shape" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 22, "id": "561a5589", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "outputs": [ { "data": { "text/plain": [ "{'S_I': 30.0,\n", - " 'S_S': 2.8098364831332874,\n", - " 'X_I': 1147.9022739122495,\n", - " 'X_S': 82.1499821192233,\n", - " 'X_BH': 2551.1711914474663,\n", - " 'X_BA': 148.18618671733967,\n", - " 'X_P': 447.1254556866469,\n", - " 'S_O': 0.004289034729931556,\n", - " 'S_NO': 5.338805118093289,\n", - " 'S_NH': 7.929128379209027,\n", - " 'S_ND': 1.2166810265512678,\n", - " 'X_ND': 5.285761329880132,\n", - " 'S_ALK': 59.15859597894533,\n", - " 'S_N2': 25.007887255081272,\n", - " 'H2O': 998557.4809730583,\n", + " 'S_S': 2.8098296544615704,\n", + " 'X_I': 1147.8970757884535,\n", + " 'X_S': 82.14996504835973,\n", + " 'X_BH': 2551.1712941951987,\n", + " 'X_BA': 148.18576250649838,\n", + " 'X_P': 447.1086242830684,\n", + " 'S_O': 0.004288622012845044,\n", + " 'S_NO': 5.33892893863284,\n", + " 'S_NH': 7.928812844268634,\n", + " 'S_ND': 1.216680910568711,\n", + " 'X_ND': 5.285760801254182,\n", + " 'S_ALK': 59.158219028756534,\n", + " 'S_N2': 25.008073542375985,\n", + " 'H2O': 997794.331078558,\n", " 'Q': 92229.99999999996}" ] }, - "execution_count": 24, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -1387,7 +1236,11 @@ { "cell_type": "markdown", "id": "68f067f1", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "skip" + } + }, "source": [ "[Back to top](#top)" ] @@ -1395,11 +1248,25 @@ { "cell_type": "markdown", "id": "b6a928f2", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "source": [ "### 2.2. Fundamental methods\n", - "In addition to proper `__init__` and `_run` methods ([recap](https://qsdsan.readthedocs.io/en/latest/tutorials/5_SanUnit_advanced.html#2.1.-Fundamental-methods)), a few more methods are required in a `SanUnit` subclass for dynamic simulation. Users typically won't interact with these methods but they will be called by `System.simulate` to manipulate the values of the arrays mentioned [above](#s2.1) (i.e., `._state`, `._dstate`, `.state`, and `.dstate`).\n", - "\n", + "In addition to proper `__init__` and `_run` methods ([recap](https://qsdsan.readthedocs.io/en/latest/tutorials/5_SanUnit_advanced.html#2.1.-Fundamental-methods)), a few more methods are required in a `SanUnit` subclass for dynamic simulation. Users typically won't interact with these methods but they will be called by `System.simulate` to manipulate the values of the arrays mentioned [above](#s2.1) (i.e., `._state`, `._dstate`, `.state`, and `.dstate`)." + ] + }, + { + "cell_type": "markdown", + "id": "976dabeb", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ "- `_init_state`, called after `_run` to generate an initial condition for the unit, i.e., defining shape and values of the `_state` and `_dstate` arrays. For example:\n", "```python\n", "import numpy as np\n", @@ -1408,9 +1275,18 @@ " self._state = np.ones(len(inf.components)+1)\n", " self._dstate = self._state * 0.\n", "```\n", - "This method (not saying it makes sense) assumes $n+1$ state variables and gives an initial value of 1 to all of them. Then it also sets the initial time derivatives to be 0. \n", - "\n", - "\n", + "This method (not saying it makes sense) assumes $n+1$ state variables and gives an initial value of 1 to all of them. Then it also sets the initial time derivatives to be 0. " + ] + }, + { + "cell_type": "markdown", + "id": "3a3de71d", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ "- `_update_state`, to update effluent streams' state arrays based on current state (and maybe dstate) of the SanUnit. For example:\n", "```python\n", "def _update_state(self):\n", @@ -1418,8 +1294,18 @@ " eff, = self.outs # assuming this SanUnit has one outlet only\n", " eff.state[:] = arr # assume arr has the same shape as WasteStream.state\n", "```\n", - "The goal of this method is to update the values in `.state` for each `WasteStream` in `.outs`.\n", - "\n", + "The goal of this method is to update the values in `.state` for each `WasteStream` in `.outs`." + ] + }, + { + "cell_type": "markdown", + "id": "8deec2a2", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ "- `_update_dstate`, to update effluent streams' `dstate` arrays based on current `_state` and `_dstate` of the SanUnit. The signiture and often the algorithm are similar to `_update_state`.\n", "\n", "\n", @@ -1430,7 +1316,18 @@ " if self._ODE is None:\n", " self._compile_ODE()\n", " return self._ODE \n", - "```\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "a431142f", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ "```python\n", "def _compile_ODE(self):\n", " _dstate = self._dstate\n", @@ -1439,14 +1336,36 @@ " _dstate[:] = some_algorithm(t, y_ins, y, dy_ins)\n", " _update_dstate()\n", " self._ODE = dy_dt\n", - "```\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "83c50a89", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ "```python\n", "@property\n", "def AE(self):\n", " if self._AE is None:\n", " self._compile_AE()\n", " return self._AE\n", - "```\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "dd66c263", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ "```python\n", "def _compile_AE(self):\n", " _state = self._state\n", @@ -1459,8 +1378,18 @@ " _update_state()\n", " _update_dstate()\n", " self._AE = y_t\n", - "```\n", - "\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "a144502d", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ "> **Note**: Within the `dy_dt` or `y_t` functions, `._state[:] = ` rather than `._state = ` because it's generally faster to update values in an existing array than overwriting this array with a newly created array.\n", "\n", "We'll learn more about these two methods in the next subsections." @@ -1469,7 +1398,11 @@ { "cell_type": "markdown", "id": "7cb3c766", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "skip" + } + }, "source": [ "[Back to top](#top)" ] @@ -1477,7 +1410,11 @@ { "cell_type": "markdown", "id": "afd475f2", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "source": [ "### 2.3. Making a simple MixerSplitter (`_compile_AE`)\n", "\n", @@ -1486,9 +1423,13 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 23, "id": "c38b235a", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "outputs": [], "source": [ "# Typically if implemented as a static SanUnit, it'd be pretty simple\n", @@ -1520,9 +1461,13 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 24, "id": "9b5ce52d", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "outputs": [ { "name": "stdout", @@ -1540,34 +1485,37 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 25, "id": "12aa03d9", "metadata": { - "scrolled": false + "scrolled": false, + "slideshow": { + "slide_type": "slide" + } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "WasteStream: ws28\n", - " phase: 'l', T: 298.15 K, P: 101325 Pa\n", - " flow (g/hr): S_S 3e+03\n", - " S_NH 2.1e+03\n", - " H2O 8e+05\n", + "WasteStream: ws12\n", + "phase: 'l', T: 298.15 K, P: 101325 Pa\n", + "flow (g/hr): S_S 3e+03\n", + " S_NH 2.1e+03\n", + " H2O 8e+05\n", " WasteStream-specific properties:\n", " pH : 7.0\n", " Alkalinity : 2.5 mg/L\n", - " COD : 3711.6 mg/L\n", - " BOD : 2661.2 mg/L\n", - " TC : 1187.7 mg/L\n", - " TOC : 1187.7 mg/L\n", - " TN : 2598.1 mg/L\n", + " COD : 3711.8 mg/L\n", + " BOD : 2661.3 mg/L\n", + " TC : 1187.8 mg/L\n", + " TOC : 1187.8 mg/L\n", + " TN : 2598.2 mg/L\n", " TP : 37.1 mg/L\n", " Component concentrations (mg/L):\n", - " S_S 3711.6\n", - " S_NH 2598.1\n", - " H2O 989764.3\n" + " S_S 3711.8\n", + " S_NH 2598.2\n", + " H2O 989803.5\n" ] } ], @@ -1580,79 +1528,82 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 26, "id": "4ff4c667", "metadata": { - "scrolled": true + "scrolled": true, + "slideshow": { + "slide_type": "slide" + } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "MixerSplitter1: M2\n", + "MixerSplitter1: M1\n", "ins...\n", - "[0] ws27\n", - " phase: 'l', T: 298.15 K, P: 101325 Pa\n", - " flow (g/hr): S_O 5e+03\n", - " H2O 1e+06\n", + "[0] ws11\n", + "phase: 'l', T: 298.15 K, P: 101325 Pa\n", + "flow (g/hr): S_O 5e+03\n", + " H2O 1e+06\n", " WasteStream-specific properties:\n", " pH : 7.0\n", - "[1] ws28\n", - " phase: 'l', T: 298.15 K, P: 101325 Pa\n", - " flow (g/hr): S_S 3e+03\n", - " S_NH 2.1e+03\n", - " H2O 8e+05\n", + "[1] ws12\n", + "phase: 'l', T: 298.15 K, P: 101325 Pa\n", + "flow (g/hr): S_S 3e+03\n", + " S_NH 2.1e+03\n", + " H2O 8e+05\n", " WasteStream-specific properties:\n", " pH : 7.0\n", - " COD : 3711.6 mg/L\n", - " BOD : 2661.2 mg/L\n", - " TC : 1187.7 mg/L\n", - " TOC : 1187.7 mg/L\n", - " TN : 2598.1 mg/L\n", + " COD : 3711.8 mg/L\n", + " BOD : 2661.3 mg/L\n", + " TC : 1187.8 mg/L\n", + " TOC : 1187.8 mg/L\n", + " TN : 2598.2 mg/L\n", " TP : 37.1 mg/L\n", "outs...\n", - "[0] ws29\n", - " phase: 'l', T: 298.15 K, P: 101325 Pa\n", - " flow (g/hr): S_S 1e+03\n", - " S_O 1.67e+03\n", - " S_NH 700\n", - " H2O 6e+05\n", + "[0] ws13\n", + "phase: 'l', T: 298.15 K, P: 101325 Pa\n", + "flow (g/hr): S_S 1e+03\n", + " S_O 1.67e+03\n", + " S_NH 700\n", + " H2O 6e+05\n", " WasteStream-specific properties:\n", " pH : 7.0\n", - " COD : 1645.9 mg/L\n", - " BOD : 1180.1 mg/L\n", - " TC : 526.7 mg/L\n", - " TOC : 526.7 mg/L\n", - " TN : 1152.1 mg/L\n", + " COD : 1650.8 mg/L\n", + " BOD : 1183.6 mg/L\n", + " TC : 528.3 mg/L\n", + " TOC : 528.3 mg/L\n", + " TN : 1155.6 mg/L\n", " TP : 16.5 mg/L\n", - "[1] ws30\n", - " phase: 'l', T: 298.15 K, P: 101325 Pa\n", - " flow (g/hr): S_S 1e+03\n", - " S_O 1.67e+03\n", - " S_NH 700\n", - " H2O 6e+05\n", + "[1] ws14\n", + "phase: 'l', T: 298.15 K, P: 101325 Pa\n", + "flow (g/hr): S_S 1e+03\n", + " S_O 1.67e+03\n", + " S_NH 700\n", + " H2O 6e+05\n", " WasteStream-specific properties:\n", " pH : 7.0\n", - " COD : 1645.9 mg/L\n", - " BOD : 1180.1 mg/L\n", - " TC : 526.7 mg/L\n", - " TOC : 526.7 mg/L\n", - " TN : 1152.1 mg/L\n", + " COD : 1650.8 mg/L\n", + " BOD : 1183.6 mg/L\n", + " TC : 528.3 mg/L\n", + " TOC : 528.3 mg/L\n", + " TN : 1155.6 mg/L\n", " TP : 16.5 mg/L\n", - "[2] ws31\n", - " phase: 'l', T: 298.15 K, P: 101325 Pa\n", - " flow (g/hr): S_S 1e+03\n", - " S_O 1.67e+03\n", - " S_NH 700\n", - " H2O 6e+05\n", + "[2] ws15\n", + "phase: 'l', T: 298.15 K, P: 101325 Pa\n", + "flow (g/hr): S_S 1e+03\n", + " S_O 1.67e+03\n", + " S_NH 700\n", + " H2O 6e+05\n", " WasteStream-specific properties:\n", " pH : 7.0\n", - " COD : 1645.9 mg/L\n", - " BOD : 1180.1 mg/L\n", - " TC : 526.7 mg/L\n", - " TOC : 526.7 mg/L\n", - " TN : 1152.1 mg/L\n", + " COD : 1650.8 mg/L\n", + " BOD : 1183.6 mg/L\n", + " TC : 528.3 mg/L\n", + " TOC : 528.3 mg/L\n", + " TN : 1155.6 mg/L\n", " TP : 16.5 mg/L\n" ] } @@ -1665,9 +1616,14 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 27, "id": "72151a1e", - "metadata": {}, + "metadata": { + "scrolled": true, + "slideshow": { + "slide_type": "slide" + } + }, "outputs": [], "source": [ "# Obviously, it's not ready for dynamic simulation\n", @@ -1679,30 +1635,59 @@ { "cell_type": "markdown", "id": "0c4eb0cd", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "source": [ "Since the mixer-splitter mixes and splits instantly, we can express this process with a set of algebraic equations (AEs). Assume its array of state variables follow the \"concentration-volumetric flow\" convention. In mathematical forms, state variables of the mixer-splitter ($C_m$, component concentrations; $Q_m$, total volumetric flow) follow:\n", "$$Q_m = \\sum_{i \\in ins} Q_i \\tag{1}$$\n", "$$Q_mC_m = \\sum_{i \\in ins} Q_iC_i$$\n", - "$$\\therefore C_m = \\frac{\\sum_{i \\in ins} Q_iC_i}{Q_m} \\tag{2}$$\n", + "$$\\therefore C_m = \\frac{\\sum_{i \\in ins} Q_iC_i}{Q_m} \\tag{2}$$" + ] + }, + { + "cell_type": "markdown", + "id": "a37f98d9", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ "Therefore, the time derivatives $\\dot{Q_m}$ follow:\n", "$$\\dot{Q_m} = \\sum_{i \\in ins} \\dot{Q_i} \\tag{3}$$\n", "$$Q_m\\dot{C_m} + C_m\\dot{Q_m} = \\sum_{i \\in ins} (Q_i\\dot{C_i} + C_i\\dot{Q_i})$$\n", - "$$\\therefore \\dot{C_m} = \\frac{1}{Q_m}\\cdot(\\sum_{i \\in ins}Q_i\\dot{C_i} + \\sum_{i \\in ins}C_i\\dot{Q_i} - C_m\\dot{Q_m}) \\tag{4}$$\n", + "$$\\therefore \\dot{C_m} = \\frac{1}{Q_m}\\cdot(\\sum_{i \\in ins}Q_i\\dot{C_i} + \\sum_{i \\in ins}C_i\\dot{Q_i} - C_m\\dot{Q_m}) \\tag{4}$$" + ] + }, + { + "cell_type": "markdown", + "id": "7578a12e", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ "For any effluent `WasteStream` $j$:\n", "$$Q_j = \\frac{Q_m}{n_{outs}} \\tag{5}$$\n", "$$C_j = C_m \\tag{6}$$\n", "$$\\therefore \\dot{Q_j} = \\frac{\\dot{Q_m}}{n_{outs}} \\tag{7}$$\n", "$$\\dot{C_j} = \\dot{C_m} \\tag{8}$$\n", - "Now, let's try to implement this algorithm in methods for dynamic simulation.\n", - "\n" + "Now, let's try to implement this algorithm in methods for dynamic simulation." ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 28, "id": "38abf7cb", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "outputs": [], "source": [ "import numpy as np\n", @@ -1758,7 +1743,11 @@ { "cell_type": "markdown", "id": "da258438", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "source": [ ">**Note**: \n", ">1. All `SanUnit._AE` must take exactly these three postional arguments (`t`, `y_ins`, `dy_ins`). `t` is time as a `float`. Both `y_ins` and `dy_ins` are **2d** `numpy.array` of the same shape `(m, n+1)`, where $m$ is the number of inlets, $n+1$ is the length of the `state` or `dstate` array of a `WasteStream`.\n", @@ -1768,9 +1757,13 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 29, "id": "ba8c9001", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "outputs": [], "source": [ "# Now let's see if this works\n", @@ -1782,13 +1775,17 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 30, "id": "a4f65bf6", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -1806,7 +1803,11 @@ { "cell_type": "markdown", "id": "22788b98", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "source": [ "Many commonly used unit operations, such as [Pump](https://qsdsan.readthedocs.io/en/latest/api/sanunits/pumping.html#qsdsan.sanunits.Pump), [Mixer](https://qsdsan.readthedocs.io/en/latest/api/sanunits/abstract.html#mixer), [Splitter](https://qsdsan.readthedocs.io/en/latest/api/sanunits/abstract.html#splitter), and [HydraulicDelay](https://qsdsan.readthedocs.io/en/latest/api/sanunits/pumping.html#hydraulicdelay), have implemented the fundamental methods to be used in a dynamic system. You can always refer to the source codes of these units to learn more about how they work." ] @@ -1814,7 +1815,11 @@ { "cell_type": "markdown", "id": "1f5d8d3f", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "skip" + } + }, "source": [ "[Back to top](#top)" ] @@ -1822,7 +1827,11 @@ { "cell_type": "markdown", "id": "a04a8ab5", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "source": [ "### 2.4. Making an inactive CompleteMixTank (`_compile_ODE`)" ] @@ -1830,7 +1839,11 @@ { "cell_type": "markdown", "id": "21dca6ff", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "source": [ "As you can see above, it's not very impressive to dynamically simulate a system without any ODEs. So let's make a simple inactive complete mix tank. Assume the reactor has a fixed liquid volume $V$, and thus the effluent volumetric flow rate changes instantly with influents. The mass balance of this type of reactor can be described as:\n", "$$Q = \\sum_{i \\in ins} Q_i \\tag{9}$$\n", @@ -1842,9 +1855,13 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 31, "id": "c4706ed2", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "outputs": [], "source": [ "class CompleteMixTank(qs.SanUnit):\n", @@ -1908,7 +1925,11 @@ { "cell_type": "markdown", "id": "472f1577", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "source": [ ">**Note**: \n", ">1. All `SanUnit._ODE` must take exactly these four postional arguments: `t`, `y_ins`, and `dy_ins` are the same as the ones in `SanUnit._AE`. `y` is a **1d** `numpy.array`, because it is equal to the `_state` array of the unit.\n", @@ -1918,9 +1939,13 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 32, "id": "493239c1", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "outputs": [], "source": [ "# Let's see if it works\n", @@ -1937,24 +1962,28 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 33, "id": "c3df8f02", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "outputs": [ { "data": { "text/plain": [ "(
,\n", - " )" + " )" ] }, - "execution_count": 34, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1970,7 +1999,11 @@ { "cell_type": "markdown", "id": "3970aeaa", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "source": [ "Many commonly used unit operations described by ODEs have been implemented in QSDsan, such as [CSTR](https://qsdsan.readthedocs.io/en/latest/api/sanunits/suspended_growth_bioreactors.html#cstr), [BatchExperiment](https://qsdsan.readthedocs.io/en/latest/api/sanunits/suspended_growth_bioreactors.html#batchexperiment), and [FlatBottomCircularClarifier](https://qsdsan.readthedocs.io/en/latest/api/sanunits/clarifiers.html)." ] @@ -1978,7 +2011,11 @@ { "cell_type": "markdown", "id": "9d9a485a", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "skip" + } + }, "source": [ "[Back to top](#top)" ] @@ -1986,7 +2023,11 @@ { "cell_type": "markdown", "id": "b085b491", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "source": [ "## 3. Other convenient features \n", "### 3.1. `ExogenousDynamicVariable`\n", @@ -1995,42 +2036,75 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 34, "id": "6e8b6a32", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "outputs": [], "source": [ "# Check out the documentation\n", "from qsdsan.utils import ExogenousDynamicVariable as EDV\n", - "EDV?" + "# EDV?" ] }, { "cell_type": "markdown", "id": "d0365c64", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "source": [ "There are generally two ways to create an `ExogenousDynamicVariable`.\n", "\n", "1. __Define the variable as a function of time.__ Let's say we want to create a variable to represent the changing reaction temperature. Assume the temperature value \\[K\\] can be expressed as $T = 298.15 + 5\\cdot \\sin(t)$, indicating that the temperatue fluctuacts around $25^{\\circ}C$ by $\\pm 5^{\\circ}C$. Then simply,\n", "```python\n", "T = EDV('T', function=lambda t: 298.15+5*np.sin(t))\n", - "```\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "e2885b32", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ "2. __Provide time-series data to describe the dynamics of the variable.__ For demonstration purpose, we'll just make up the data. In practice, this is convenient if you have real data.\n", "```python\n", "t_arr = np.linspace(0, 5)\n", "y_arr = 298.15+5*np.sin(t_arr)\n", "T = EDV('T', t=t_arr, y=y_arr)\n", - "```\n", - "\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "a89fa738", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ "For convenience, `ExogenousDynamicVariable` also has a `classmethod` that enables batch creation of multiple variables at once. We just need to provide a file of the time-series data, including a column `t` for time points and additional columns of the variable values. See the [documentation](https://qsdsan.readthedocs.io/en/latest/api/utils/dynamics.html#qsdsan.utils.ExogenousDynamicVariable.batch_init) of `ExogenousDynamicVariable.batch_init` for detailed usage." ] }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 35, "id": "b6401d1c", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "outputs": [], "source": [ "# EDV.batch_init?" @@ -2039,7 +2113,11 @@ { "cell_type": "markdown", "id": "8a5bd5b7", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "source": [ "Once created, these `ExogenousDynamicVariable` objects can be incorporated into any `SanUnit` upon its initialization or through the `SanUnit.exo_dynamic_vars` property setter. " ] @@ -2048,12 +2126,24 @@ "cell_type": "code", "execution_count": 36, "id": "2639a8b7", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "All impact indicators have been removed from the registry.\n", + "All impact items have been removed from the registry.\n" + ] + }, { "data": { "text/plain": [ - "(, )" + "(,)" ] }, "execution_count": 36, @@ -2063,22 +2153,26 @@ ], "source": [ "# Let's see an example\n", - "from exposan.metab_mock import create_systems\n", - "sys_mt, = create_systems(which='A')\n", + "from exposan.metab import create_system\n", + "sys_mt = create_system()\n", "uf_mt = sys_mt.flowsheet.unit\n", - "uf_mt.R1A.exo_dynamic_vars" + "uf_mt.R1.exo_dynamic_vars" ] }, { "cell_type": "code", "execution_count": 37, "id": "a7e11837", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "outputs": [ { "data": { "text/plain": [ - "[308.15, 5.8]" + "[295.15]" ] }, "execution_count": 37, @@ -2089,13 +2183,17 @@ "source": [ "# The evaluation of these variables during unit simulation is done through \n", "# the `eval_exo_dynamic_vars` method\n", - "uf_mt.R1A.eval_exo_dynamic_vars(t=0.1)" + "uf_mt.R1.eval_exo_dynamic_vars(t=0.1)" ] }, { "cell_type": "markdown", "id": "0d1b7290", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "skip" + } + }, "source": [ "[Back to top](#top)" ] @@ -2103,7 +2201,11 @@ { "cell_type": "markdown", "id": "d8205f55", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "source": [ "### 3.2. `DynamicInfluent`\n", "The [DynamicInfluent](https://qsdsan.readthedocs.io/en/latest/api/sanunits/DynamicInfluent.html) is a `SanUnit` subclass for generating dynamic influent streams from user-defined time-series data. The use of this class is, to some extent, similar to an `ExogenousDynamicVariable`." @@ -2113,7 +2215,11 @@ "cell_type": "code", "execution_count": 38, "id": "3ea577e7", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "outputs": [], "source": [ "from qsdsan.sanunits import DynamicInfluent as DI\n", @@ -2124,13 +2230,18 @@ "cell_type": "code", "execution_count": 39, "id": "7c2c9521", - "metadata": {}, + "metadata": { + "scrolled": false, + "slideshow": { + "slide_type": "slide" + } + }, "outputs": [ { "data": { "text/plain": [ "(
,\n", - " )" + " )" ] }, "execution_count": 39, @@ -2139,7 +2250,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -2160,17 +2271,22 @@ { "cell_type": "markdown", "id": "786d6034", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "skip" + } + }, "source": [ "[Back to top](#top)" ] } ], "metadata": { + "celltoolbar": "Slideshow", "kernelspec": { - "display_name": "Python [conda env:tut]", + "display_name": "Python 3 (ipykernel)", "language": "python", - "name": "conda-env-tut-py" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -2182,7 +2298,36 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.0" + "version": "3.9.13" + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false } }, "nbformat": 4, diff --git a/docs/source/tutorials/12_Anaerobic_Digestion_Model_No_1.ipynb b/docs/source/tutorials/12_Anaerobic_Digestion_Model_No_1.ipynb new file mode 100644 index 00000000..b986a473 --- /dev/null +++ b/docs/source/tutorials/12_Anaerobic_Digestion_Model_No_1.ipynb @@ -0,0 +1,1538 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "8d891055", + "metadata": {}, + "source": [ + "# Anaerobic Digestion Model No. 1 (ADM1) \n", + "\n", + "- **Prepared by:**\n", + " \n", + " - [Ga-Yeong Kim](https://qsdsan.readthedocs.io/en/latest/CONTRIBUTING.html)\n", + " \n", + "- **Covered topics:**\n", + "\n", + " - [1. Introduction](#s1)\n", + " - [2. System Setup](#s2)\n", + " - [3. System Simulation](#s3)\n", + " \n", + "To run tutorials in your browser, go to this [Binder page](https://mybinder.org/v2/gh/QSD-Group/QSDsan-env/main?urlpath=git-pull%3Frepo%3Dhttps%253A%252F%252Fgithub.com%252FQSD-group%252FQSDsan%26urlpath%3Dtree%252FQSDsan%252Fdocs%252Fsource%252Ftutorials%26branch%3Dmain)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "9a2a96b7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "This tutorial was made with qsdsan v1.3.1 and exposan v1.3.1\n" + ] + } + ], + "source": [ + "import qsdsan as qs, exposan\n", + "print(f'This tutorial was made with qsdsan v{qs.__version__} and exposan v{exposan.__version__}')" + ] + }, + { + "cell_type": "markdown", + "id": "1bbdffaa", + "metadata": {}, + "source": [ + "## 1. Introduction " + ] + }, + { + "cell_type": "markdown", + "id": "cefa6e0a", + "metadata": {}, + "source": [ + "Anaerobic Digestion Model No.1 (ADM1) includes multiple steps describing **biochemical** as well as **physicochemical processes**. \n", + "\n", + "The **biochemical steps** include disintegration from homogeneous particulates to carbohydrates, proteins and lipids; extracellular hydrolysis of these particulate substrates to sugars, amino acids, and long chain fatty acids (LCFA), respectively; acidogenesis from sugars and amino acids to volatile fatty acids (VFAs) and hydrogen; acetogenesis of LCFA and VFAs to acetate; and separate methanogenesis steps from acetate and hydrogen/CO2. \n", + "\n", + "The **physico-chemical equations** describe ion association and dissociation, and gas-liquid transfer. \n", + "\n", + "Implemented as a differential and algebraic equation (DAE) set, there are 26 dynamic state concentration variables, and 8 implicit algebraic variables per reactor vessel or element. Implemented as differential equations (DE) only, there are 32 dynamic concentration state variables.\n", + "\n", + "*Water Science and Technology, Vol 45, No 10, pp 65–73*" + ] + }, + { + "attachments": { + "ADM1.JPG": { + "image/jpeg": 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+ } + }, + "cell_type": "markdown", + "id": "180af880", + "metadata": {}, + "source": [ + "![assets/ADM1.JPG](attachment:ADM1.JPG)" + ] + }, + { + "cell_type": "markdown", + "id": "deab6410", + "metadata": {}, + "source": [ + "**Note:** You can find validation of the ADM1 system in [EXPOsan](https://github.com/QSD-Group/EXPOsan/tree/main/exposan/adm)." + ] + }, + { + "cell_type": "markdown", + "id": "47af6e27", + "metadata": {}, + "source": [ + "## 2. System Setup " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "fb4e6486", + "metadata": {}, + "outputs": [], + "source": [ + "# Import packages\n", + "import numpy as np\n", + "from chemicals.elements import molecular_weight as get_mw\n", + "from qsdsan import sanunits as su, processes as pc, WasteStream, System\n", + "from qsdsan.utils import time_printer\n", + "\n", + "import warnings\n", + "warnings.simplefilter(action='ignore', category=FutureWarning) # to ignore Pandas future warning" + ] + }, + { + "cell_type": "markdown", + "id": "8c7244dc", + "metadata": {}, + "source": [ + "### 2.1. State variables of ADM1" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "5774fdae", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CompiledComponents([S_su, S_aa, S_fa, S_va, S_bu, S_pro, S_ac, S_h2, S_ch4, S_IC, S_IN, S_I, X_c, X_ch, X_pr, X_li, X_su, X_aa, X_fa, X_c4, X_pro, X_ac, X_h2, X_I, S_cat, S_an, H2O])\n" + ] + } + ], + "source": [ + "# Components \n", + "cmps = pc.create_adm1_cmps() # create state variables for ADM1\n", + "cmps.show() # 26 components in ADM1 + water" + ] + }, + { + "cell_type": "markdown", + "id": "4ee7c0b5", + "metadata": {}, + "source": [ + "**S_su**: Monosaccharides, **S_aa**: Amino acids, **S_fa**: Total long-chain fatty acids, **S_va**: Total valerate, **S_bu**: Total butyrate, **S_pro**: Total propionate, **S_ac**: Total acetate, **S_h2**: Hydrogen gas, **S_ch4**: Methane gas, **S_IC**: Inorganic carbon, **S_IN**: Inorganic nitrogen, **S_I**: Soluble inerts, **X_c**: Composites, **X_ch**: Carobohydrates, **X_pr**: Proteins, **X_li**: Lipids, **X_su**: Biomass uptaking sugars, **X_aa**: Biomass uptaking amino acids, **X_fa**: Biomass uptaking long chain fatty acids, **X_c4**: Biomass uptaking c4 fatty acids (valerate and butyrate), **X_pro**: Biomass uptaking propionate, **X_ac**: Biomass uptaking acetate, **X_h2**: Biomass uptaking hydrogen, **X_I**: Particulate inerts, **S_cat**: Other cations, **S_an**: Other anions" + ] + }, + { + "cell_type": "markdown", + "id": "c4f28ea2", + "metadata": {}, + "source": [ + "### 2.2. The ADM1 `Process`" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "0dd6a5b8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ADM1([disintegration, hydrolysis_carbs, hydrolysis_proteins, hydrolysis_lipids, uptake_sugars, uptake_amino_acids, uptake_LCFA, uptake_valerate, uptake_butyrate, uptake_propionate, uptake_acetate, uptake_h2, decay_Xsu, decay_Xaa, decay_Xfa, decay_Xc4, decay_Xpro, decay_Xac, decay_Xh2, h2_transfer, ch4_transfer, IC_transfer])\n" + ] + } + ], + "source": [ + "# Processes\n", + "adm1 = pc.ADM1() # create ADM1 processes\n", + "adm1.show() # 22 processes in ADM1" + ] + }, + { + "cell_type": "markdown", + "id": "0b3d103f", + "metadata": {}, + "source": [ + "### 2.3. Petersen matrix of ADM1" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "9a9db08e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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S_suS_aaS_faS_vaS_bu...X_h2X_IS_catS_anH2O
disintegration00000...00.2000
hydrolysis_carbs10000...00000
hydrolysis_proteins01000...00000
hydrolysis_lipids0.0500.9500...00000
uptake_sugars-10000.117...00000
uptake_amino_acids0-100.2120.239...00000
uptake_LCFA00-100...00000
uptake_valerate000-10...00000
uptake_butyrate0000-1...00000
uptake_propionate00000...00000
uptake_acetate00000...00000
uptake_h200000...0.060000
decay_Xsu00000...00000
decay_Xaa00000...00000
decay_Xfa00000...00000
decay_Xc400000...00000
decay_Xpro00000...00000
decay_Xac00000...00000
decay_Xh200000...-10000
h2_transfer00000...00000
ch4_transfer00000...00000
IC_transfer00000...00000
\n", + "

22 rows × 27 columns

\n", + "
" + ], + "text/plain": [ + " S_su S_aa S_fa S_va S_bu ... X_h2 X_I S_cat S_an H2O\n", + "disintegration 0 0 0 0 0 ... 0 0.2 0 0 0\n", + "hydrolysis_carbs 1 0 0 0 0 ... 0 0 0 0 0\n", + "hydrolysis_proteins 0 1 0 0 0 ... 0 0 0 0 0\n", + "hydrolysis_lipids 0.05 0 0.95 0 0 ... 0 0 0 0 0\n", + "uptake_sugars -1 0 0 0 0.117 ... 0 0 0 0 0\n", + "uptake_amino_acids 0 -1 0 0.212 0.239 ... 0 0 0 0 0\n", + "uptake_LCFA 0 0 -1 0 0 ... 0 0 0 0 0\n", + "uptake_valerate 0 0 0 -1 0 ... 0 0 0 0 0\n", + "uptake_butyrate 0 0 0 0 -1 ... 0 0 0 0 0\n", + "uptake_propionate 0 0 0 0 0 ... 0 0 0 0 0\n", + "uptake_acetate 0 0 0 0 0 ... 0 0 0 0 0\n", + "uptake_h2 0 0 0 0 0 ... 0.06 0 0 0 0\n", + "decay_Xsu 0 0 0 0 0 ... 0 0 0 0 0\n", + "decay_Xaa 0 0 0 0 0 ... 0 0 0 0 0\n", + "decay_Xfa 0 0 0 0 0 ... 0 0 0 0 0\n", + "decay_Xc4 0 0 0 0 0 ... 0 0 0 0 0\n", + "decay_Xpro 0 0 0 0 0 ... 0 0 0 0 0\n", + "decay_Xac 0 0 0 0 0 ... 0 0 0 0 0\n", + "decay_Xh2 0 0 0 0 0 ... -1 0 0 0 0\n", + "h2_transfer 0 0 0 0 0 ... 0 0 0 0 0\n", + "ch4_transfer 0 0 0 0 0 ... 0 0 0 0 0\n", + "IC_transfer 0 0 0 0 0 ... 0 0 0 0 0\n", + "\n", + "[22 rows x 27 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Petersen stoichiometric matrix\n", + "adm1.stoichiometry" + ] + }, + { + "cell_type": "markdown", + "id": "4d14e88c", + "metadata": {}, + "source": [ + "**The rate of production or consumption for a state variable**
\n", + "\n", + "$a_{ij}$: the stoichiometric coefficient of component $j$ in process $i$ (i.e., value on the $i$th row and $j$th column of the stoichiometry matrix)
\n", + "$\\rho_i$: process $i$'s reaction rate
\n", + "$r_j$: the overall production or consumption rate of component $j$
\n", + "$$r_j = \\sum_i{a_{ij}\\cdot\\rho_i}$$\n", + "In matrix notation, this calculation can be neatly described as\n", + "$$\\mathbf{r} = \\mathbf{A^T} \\mathbf{\\rho}$$\n", + "where $\\mathbf{A}$ is the stoichiometry matrix and $\\mathbf{\\rho}$ is the array of process rates." + ] + }, + { + "cell_type": "markdown", + "id": "e2c2360d", + "metadata": {}, + "source": [ + "### 2.4. Influent & effluent" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "a28bc7d2", + "metadata": {}, + "outputs": [], + "source": [ + "# Flow rate, temperature, HRT\n", + "Q = 170 # influent flowrate [m3/d]\n", + "Temp = 273.15+35 # temperature [K]\n", + "HRT = 5 # HRT [d]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "28a9c8e5", + "metadata": {}, + "outputs": [], + "source": [ + "# WasteStream\n", + "inf = WasteStream('Influent', T=Temp) # influent\n", + "eff = WasteStream('Effluent', T=Temp) # effluent\n", + "gas = WasteStream('Biogas') # gas" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "bdd90569", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WasteStream: Influent\n", + "phase: 'l', T: 308.15 K, P: 101325 Pa\n", + "flow (g/hr): S_su 70.8\n", + " S_aa 7.08\n", + " S_fa 7.08\n", + " S_va 7.08\n", + " S_bu 7.08\n", + " S_pro 7.08\n", + " S_ac 7.08\n", + " S_h2 7.08e-05\n", + " S_ch4 0.0708\n", + " S_IC 3.4e+03\n", + " S_IN 992\n", + " S_I 142\n", + " X_c 1.42e+04\n", + " X_ch 3.54e+04\n", + " X_pr 1.42e+05\n", + " ... 6.97e+06\n", + " WasteStream-specific properties:\n", + " pH : 7.0\n", + " Alkalinity : 2.5 mg/L\n", + " COD : 57096.0 mg/L\n", + " BOD : 12769.4 mg/L\n", + " TC : 20596.5 mg/L\n", + " TOC : 20116.0 mg/L\n", + " TN : 3683.2 mg/L\n", + " TP : 489.3 mg/L\n", + " TK : 9.8 mg/L\n", + " Component concentrations (mg/L):\n", + " S_su 10.0\n", + " S_aa 1.0\n", + " S_fa 1.0\n", + " S_va 1.0\n", + " S_bu 1.0\n", + " S_pro 1.0\n", + " S_ac 1.0\n", + " S_h2 0.0\n", + " S_ch4 0.0\n", + " S_IC 480.4\n", + " S_IN 140.1\n", + " S_I 20.0\n", + " X_c 2000.0\n", + " X_ch 5000.0\n", + " X_pr 20000.0\n", + " ...\n" + ] + } + ], + "source": [ + "# Set influent concentration\n", + "C_mw = get_mw({'C':1}) # molecular weight of carbon\n", + "N_mw = get_mw({'N':1}) # molecular weight of nitrogen\n", + "\n", + "default_inf_kwargs = {\n", + " 'concentrations': {\n", + " 'S_su':0.01,\n", + " 'S_aa':1e-3,\n", + " 'S_fa':1e-3,\n", + " 'S_va':1e-3,\n", + " 'S_bu':1e-3,\n", + " 'S_pro':1e-3,\n", + " 'S_ac':1e-3,\n", + " 'S_h2':1e-8,\n", + " 'S_ch4':1e-5,\n", + " 'S_IC':0.04*C_mw,\n", + " 'S_IN':0.01*N_mw,\n", + " 'S_I':0.02,\n", + " 'X_c':2.0,\n", + " 'X_ch':5.0,\n", + " 'X_pr':20.0,\n", + " 'X_li':5.0,\n", + " 'X_aa':1e-2,\n", + " 'X_fa':1e-2,\n", + " 'X_c4':1e-2,\n", + " 'X_pro':1e-2,\n", + " 'X_ac':1e-2,\n", + " 'X_h2':1e-2,\n", + " 'X_I':25,\n", + " 'S_cat':0.04,\n", + " 'S_an':0.02,\n", + " },\n", + " 'units': ('m3/d', 'kg/m3'),\n", + " } # concentration of each state variable in influent\n", + "\n", + "inf.set_flow_by_concentration(Q, **default_inf_kwargs) # set influent concentration\n", + "inf" + ] + }, + { + "cell_type": "markdown", + "id": "4bf9c287", + "metadata": {}, + "source": [ + "### 2.5. Reactor" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "1fc90df0", + "metadata": {}, + "outputs": [], + "source": [ + "# SanUnit\n", + "AD = su.AnaerobicCSTR('AD', ins=inf, outs=(gas, eff), model=adm1, V_liq=Q*HRT, V_gas=Q*HRT*0.1, T=Temp)" + ] + }, + { + "cell_type": "markdown", + "id": "0716d4c9", + "metadata": {}, + "source": [ + "**su.AnaerobicCSTR**(\n", + " ID='',\n", + " ins=None,\n", + " outs=(),\n", + " thermo=None,\n", + " init_with='WasteStream',\n", + " V_liq=3400,\n", + " V_gas=300,\n", + " model=None,\n", + " T=308.15,\n", + " headspace_P=1.013,\n", + " external_P=1.013,\n", + " pipe_resistance=50000.0,\n", + " fixed_headspace_P=False,\n", + " retain_cmps=(),\n", + " fraction_retain=0.95,\n", + " isdynamic=True,\n", + " exogenous_vars=(),\n", + " **kwargs,\n", + ")\n", + "\n", + "**Parameters**
\n", + "*ins* : :class:`WasteStream`,\n", + " Influent to the reactor.
\n", + "*outs* : Iterable,\n", + " Biogas and treated effluent(s).
\n", + "*V_liq* : float, optional,\n", + " Liquid-phase volume [m^3]. The default is 3400.
\n", + "*V_gas* : float, optional,\n", + " Headspace volume [m^3]. The default is 300.
\n", + "*model* : :class:`Processes`, optional,\n", + " The kinetic model, typically ADM1-like. The default is None.
\n", + "*T* : float, optional,\n", + " Operation temperature [K]. The default is 308.15.
\n", + "*headspace_P* : float, optional,\n", + " Headspace pressure, if fixed [bar]. The default is 1.013.
\n", + "*external_P* : float, optional,\n", + " External pressure, typically atmospheric pressure [bar]. The default is 1.013.
\n", + "*pipe_resistance* : float, optional,\n", + " Biogas extraction pipe resistance [m3/d/bar]. The default is 5.0e4.
\n", + "*fixed_headspace_P* : bool, optional,\n", + " Whether to assume fixed headspace pressure. The default is False.
\n", + "*retain_cmps* : Iterable[str], optional,\n", + " IDs of the components that are assumed to be retained in the reactor, ideally.\n", + " The default is ().
\n", + "*fraction_retain* : float, optional,\n", + " The assumed fraction of ideal retention of select components. The default is 0.95.
" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "4d403072", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "AD\n", + "Anaerobic CSTR:c->179376415616:w\n", + "\n", + "\n", + " Biogas\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "AD\n", + "Anaerobic CSTR:c->179376414536:w\n", + "\n", + "\n", + " Effluent\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "179376414696:e->AD\n", + "Anaerobic CSTR:c\n", + "\n", + "\n", + " Influent\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "AD\n", + "Anaerobic CSTR\n", + "\n", + "\n", + "AD\n", + "Anaerobic CSTR\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "179376414696\n", + "\n", + "\n", + "\n", + "\n", + "179376415616\n", + "\n", + "\n", + "\n", + "\n", + "179376414536\n", + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AnaerobicCSTR: AD\n", + "ins...\n", + "[0] Influent\n", + "phase: 'l', T: 308.15 K, P: 101325 Pa\n", + "flow (g/hr): S_su 70.8\n", + " S_aa 7.08\n", + " S_fa 7.08\n", + " S_va 7.08\n", + " S_bu 7.08\n", + " S_pro 7.08\n", + " S_ac 7.08\n", + " S_h2 7.08e-05\n", + " S_ch4 0.0708\n", + " S_IC 3.4e+03\n", + " S_IN 992\n", + " S_I 142\n", + " X_c 1.42e+04\n", + " X_ch 3.54e+04\n", + " X_pr 1.42e+05\n", + " ... 6.97e+06\n", + " WasteStream-specific properties:\n", + " pH : 7.0\n", + " COD : 57096.0 mg/L\n", + " BOD : 12769.4 mg/L\n", + " TC : 20596.5 mg/L\n", + " TOC : 20116.0 mg/L\n", + " TN : 3683.2 mg/L\n", + " TP : 489.3 mg/L\n", + " TK : 9.8 mg/L\n", + "outs...\n", + "[0] Biogas\n", + "phase: 'l', T: 298.15 K, P: 101325 Pa\n", + "flow: 0\n", + " WasteStream-specific properties: None for empty waste streams\n", + "[1] Effluent\n", + "phase: 'l', T: 308.15 K, P: 101325 Pa\n", + "flow: 0\n", + " WasteStream-specific properties: None for empty waste streams\n" + ] + } + ], + "source": [ + "AD # anaerobic CSTR with influent, effluent, and biogas\n", + " # before running the simulation, 'outs' have nothing" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "b162ac79", + "metadata": {}, + "outputs": [], + "source": [ + "# Set initial condition of the reactor\n", + "default_init_conds = {\n", + " 'S_su': 0.0124*1e3,\n", + " 'S_aa': 0.0055*1e3,\n", + " 'S_fa': 0.1074*1e3,\n", + " 'S_va': 0.0123*1e3,\n", + " 'S_bu': 0.0140*1e3,\n", + " 'S_pro': 0.0176*1e3,\n", + " 'S_ac': 0.0893*1e3,\n", + " 'S_h2': 2.5055e-7*1e3,\n", + " 'S_ch4': 0.0555*1e3,\n", + " 'S_IC': 0.0951*C_mw*1e3,\n", + " 'S_IN': 0.0945*N_mw*1e3,\n", + " 'S_I': 0.1309*1e3,\n", + " 'X_ch': 0.0205*1e3,\n", + " 'X_pr': 0.0842*1e3,\n", + " 'X_li': 0.0436*1e3,\n", + " 'X_su': 0.3122*1e3,\n", + " 'X_aa': 0.9317*1e3,\n", + " 'X_fa': 0.3384*1e3,\n", + " 'X_c4': 0.3258*1e3,\n", + " 'X_pro': 0.1011*1e3,\n", + " 'X_ac': 0.6772*1e3,\n", + " 'X_h2': 0.2848*1e3,\n", + " 'X_I': 17.2162*1e3\n", + " } # concentration of each state variable in reactor\n", + "\n", + "AD.set_init_conc(**default_init_conds) # set initial condition of AD" + ] + }, + { + "cell_type": "markdown", + "id": "051f6b47", + "metadata": {}, + "source": [ + "### 2.6. System set-up" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "85b13876", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "Influent:c->Anaerobic_Digestion\n", + "System:c\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Anaerobic_Digestion\n", + "System:c->Biogas\n", + "Effluent:c\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Influent\n", + "\n", + "\n", + "Influent\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Anaerobic_Digestion\n", + "System\n", + "\n", + "\n", + "Anaerobic_Digestion\n", + "System\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Biogas\n", + "Effluent\n", + "\n", + "\n", + "Biogas\n", + "Effluent\n", + "\n", + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "System: Anaerobic_Digestion\n", + "ins...\n", + "[0] Influent \n", + " phase: 'l', T: 308.15 K, P: 101325 Pa\n", + " flow (kmol/hr): S_su 0.000393\n", + " S_aa 0.00708\n", + " S_fa 2.76e-05\n", + " S_va 6.94e-05\n", + " S_bu 8.13e-05\n", + " S_pro 9.69e-05\n", + " S_ac 0.00012\n", + " ... 709\n", + "outs...\n", + "[0] Biogas \n", + " phase: 'l', T: 298.15 K, P: 101325 Pa\n", + " flow: 0\n", + "[1] Effluent \n", + " phase: 'l', T: 308.15 K, P: 101325 Pa\n", + " flow: 0\n" + ] + } + ], + "source": [ + "# System\n", + "sys = System('Anaerobic_Digestion', path=(AD,)) # aggregation of sanunits\n", + "sys.set_dynamic_tracker(eff, gas) # what you want to track changes in concentration\n", + "sys # before running the simulation, 'outs' have nothing" + ] + }, + { + "cell_type": "markdown", + "id": "cd84e41c", + "metadata": {}, + "source": [ + "[Back to top](#top)" + ] + }, + { + "cell_type": "markdown", + "id": "bd50264c", + "metadata": {}, + "source": [ + "## 3. System Simulation " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "132152fe", + "metadata": {}, + "outputs": [], + "source": [ + "# Simulation settings\n", + "t = 10 # total time for simulation\n", + "t_step = 0.1 # times at which to store the computed solution \n", + "\n", + "method = 'BDF' # integration method to use\n", + "# method = 'RK45'\n", + "# method = 'RK23'\n", + "# method = 'DOP853'\n", + "# method = 'Radau'\n", + "# method = 'LSODA'\n", + "\n", + "# https://docs.scipy.org/doc/scipy/reference/generated/scipy.integrate.solve_ivp.html" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "74bcbaf0", + "metadata": {}, + "outputs": [], + "source": [ + "# Run simulation\n", + "sys.simulate(state_reset_hook='reset_cache',\n", + " t_span=(0,t),\n", + " t_eval=np.arange(0, t+t_step, t_step),\n", + " method=method,\n", + " # export_state_to=f'sol_{t}d_{method}_AD.xlsx', # uncomment to export simulation result as excel file\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "55247c4c", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "Influent:c->Anaerobic_Digestion\n", + "System:c\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Anaerobic_Digestion\n", + "System:c->Effluent\n", + "Biogas:c\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Influent\n", + "\n", + "\n", + "Influent\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Anaerobic_Digestion\n", + "System\n", + "\n", + "\n", + "Anaerobic_Digestion\n", + "System\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Effluent\n", + "Biogas\n", + "\n", + "\n", + "Effluent\n", + "Biogas\n", + "\n", + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "System: Anaerobic_Digestion\n", + "ins...\n", + "[0] Influent \n", + " phase: 'l', T: 308.15 K, P: 101325 Pa\n", + " flow (kmol/hr): S_su 0.000393\n", + " S_aa 0.00708\n", + " S_fa 2.76e-05\n", + " S_va 6.94e-05\n", + " S_bu 8.13e-05\n", + " S_pro 9.69e-05\n", + " S_ac 0.00012\n", + " ... 709\n", + "outs...\n", + "[0] Biogas \n", + " phase: 'g', T: 308.15 K, P: 101325 Pa\n", + " flow (kmol/hr): S_h2 0.00119\n", + " S_ch4 8.5\n", + " S_IC 0.414\n", + " H2O 0.205\n", + "[1] Effluent \n", + " phase: 'l', T: 308.15 K, P: 101325 Pa\n", + " flow (kmol/hr): S_su 0.00164\n", + " S_aa 0.129\n", + " S_fa 0.0222\n", + " S_va 0.00332\n", + " S_bu 0.00447\n", + " S_pro 0.0106\n", + " S_ac 0.639\n", + " ... 587\n" + ] + } + ], + "source": [ + "sys # now you have 'outs' info." + ] + }, + { + "cell_type": "markdown", + "id": "7b57f738", + "metadata": {}, + "source": [ + "### 3.1. Check simulation results: Effluent" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "990d5e59", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(
,\n", + " )" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "eff.scope.plot_time_series(('S_aa', 'S_fa', 'S_va', 'S_bu', 'S_pro', 'S_ac')) # you can plot how each state variable changes over time" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "6f674fab", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(
,\n", + " )" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "eff.scope.plot_time_series(('X_aa', 'X_fa', 'X_c4', 'X_pro', 'X_ac', 'X_h2'))" + ] + }, + { + "cell_type": "markdown", + "id": "1f991148", + "metadata": {}, + "source": [ + "### 3.2. Check simulation results: Gas" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "d54aeb58", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(
,\n", + " )" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "gas.scope.plot_time_series(('S_ch4','S_IC'))" + ] + }, + { + "cell_type": "markdown", + "id": "ccde4d80", + "metadata": {}, + "source": [ + "### 3.3. Check simulation results: Total VFAs" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "56f3fad7", + "metadata": {}, + "outputs": [], + "source": [ + "# Total VFAs = 'S_va' + 'S_bu' + 'S_pro' + 'S_ac' (you can change the equations based on your assumption)\n", + "idx_vfa = cmps.indices(['S_va', 'S_bu', 'S_pro', 'S_ac'])\n", + "\n", + "t_stamp = eff.scope.time_series\n", + "\n", + "vfa = eff.scope.record[:,idx_vfa]\n", + "total_vfa = np.sum(vfa, axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "a879f514", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Total VFA [mg/l]')" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.plot(t_stamp, total_vfa)\n", + "plt.xlabel(\"Time [day]\")\n", + "plt.ylabel(\"Total VFA [mg/l]\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/source/tutorials/12_Chlorination.ipynb b/docs/source/tutorials/12_Chlorination.ipynb deleted file mode 100644 index 7b416768..00000000 --- a/docs/source/tutorials/12_Chlorination.ipynb +++ /dev/null @@ -1,1046 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "28c4658c", - "metadata": {}, - "source": [ - "# Process Design Example: Chlorination \n", - "\n", - "- **Prepared by:**\n", - " \n", - " - [Yalin Li](https://qsdsan.readthedocs.io/en/latest/authors/Yalin_Li.html)\n", - " - [Philipp Steiner](https://www.eawag.ch/en/aboutus/portrait/organisation/staff/profile/philipp-steiner/show/)\n", - " - [Eva Reynaert](https://www.eawag.ch/en/aboutus/portrait/organisation/staff/profile/eva-reynaert/show/)\n", - "\n", - "- **Covered topics:**\n", - "\n", - " - [1. Design Algorithms](#s1)\n", - " - [2. Process Algorithms](#s2)\n", - " - [3. Unit Classes](#s3)\n", - " - [4. System, TEA, and LCA](#s4)" - ] - }, - { - "cell_type": "markdown", - "id": "90d4bb2f", - "metadata": {}, - "source": [ - "---\n", - "### Note\n", - "This tutorial is under active development." - ] - }, - { - "cell_type": "markdown", - "id": "903ee36f", - "metadata": {}, - "source": [ - "---" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "8e608f02", - "metadata": {}, - "outputs": [], - "source": [ - "# Add the path to your cloned repos\n", - "import os, sys\n", - "coding_path = os.path.abspath(os.path.join(sys.path[0], '../../../../'))\n", - "for abbr in ('tmo', 'bst', 'qs'):\n", - " sys.path.append(os.path.join(coding_path, abbr))" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "3dc1138e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "This tutorial was made with qsdsan vNone.\n" - ] - } - ], - "source": [ - "import qsdsan as qs\n", - "print(f'This tutorial was made with qsdsan v{qs.__version__}.')" - ] - }, - { - "cell_type": "markdown", - "id": "efa4852f", - "metadata": {}, - "source": [ - "### Summary\n", - "In this example, we will show how we can set up a chlorination process in `QSDsan`, which would include a contact zone, mixing/storage tanks for the chemical sodium hypochlorite (NaOCl) and treated water, and pumps (contact zone, NaOCl dosing, water storage).\n", - "\n", - "The algorithms largely follows the methodoly presented in Jones et al., Life cycle environmental impacts of disinfection technologies used in small drinking water systems. *Environmental Science & Technology*, **2018**, *52* (5), 2998-3007. https://doi.org/10.1021/acs.est.7b04448" - ] - }, - { - "cell_type": "markdown", - "id": "b7f9ccfc", - "metadata": {}, - "source": [ - "## 1. Design Algorithms " - ] - }, - { - "cell_type": "markdown", - "id": "de048b24", - "metadata": {}, - "source": [ - "### 1.1. Contact zone" - ] - }, - { - "cell_type": "markdown", - "id": "ac3a9505", - "metadata": {}, - "source": [ - "In the contact zone, chlorine (in the form of NaOCl) is added and reacts with the influent stream to inactivate microorganims (e.g., viruses, bacteria, protoza). In this example, the contact zone is modeled as a serpentine tubing." - ] - }, - { - "cell_type": "markdown", - "id": "29d2f234", - "metadata": {}, - "source": [ - "To determine the amount of NaOCl to be added, we will need to calculate the CT (concentration$*$time) values required by the inactivation target." - ] - }, - { - "cell_type": "markdown", - "id": "e5e4fdaa", - "metadata": {}, - "source": [ - "Let's assume that we will use the following table from U.S. Environmental Protection Agency to determine the CT (in min-mg/L) for 4-log inactivation of viruses by free chlorine (Table B-2 on Page B-3 in this [Disinfection Profiling and Benchmarking Technical Guidance Manual](https://www.epa.gov/system/files/documents/2022-02/disprof_bench_3rules_final_508.pdf))." - ] - }, - { - "cell_type": "markdown", - "id": "c7f08d0a", - "metadata": {}, - "source": [ - "| Temperature (°C) | pH=6-9 | pH=10 |\n", - "| :-: | :-: | :-: |\n", - "| 0.5 | 12 | 90 |\n", - "| 5 | 8 | 60 |\n", - "| 10 | 6 | 45 |\n", - "| 15 | 4 | 30 |\n", - "| 20 | 3 | 22 |\n", - "| 25 | 2 | 15 |" - ] - }, - { - "cell_type": "markdown", - "id": "11e1d04c", - "metadata": {}, - "source": [ - "With the CT value, the desired contact time $T_{contact}$ can be calculated from the desired residual chlorine concentration $C_{res}$ (see Section 2.1): \n", - "$$\n", - "T_{contact}[min] = \\frac{CT [\\frac{mg*min}{L}]}{C_{res}[\\frac{mg}{L}]}\n", - "$$\n", - " \n", - "To get the required detention time $T_{DT}$, the desired contact time needs to be corrected by a baffling factor (BF) that accounts for potential short-circuiting:\n", - "\n", - "$$\n", - "T_{DT} [min] = \\frac{T_{contact}}{BF} \n", - "$$\n", - "\n", - "A BF value of 0.7 is typical for the serpentine tubing configuration." - ] - }, - { - "cell_type": "markdown", - "id": "c524fc12", - "metadata": {}, - "source": [ - "Dimensions of the serpentine tubing can then be calculated from the $T_{DT}$:\n", - "\n", - "$$\n", - "T_{DT} [min] = \\frac{L_p}{v} = L_p * \\frac{\\pi*{(\\frac{d_p}{2})^2}}{Q} = (AS*d_p) * \\frac{\\pi*{(\\frac{d_p}{2})^2}}{Q}\n", - "$$\n", - "\n", - "where:\n", - "- $L_p$ and $d_p$ are the length and diameter of the pipe (both in m), respectively\n", - "- AS is the aspect ratio as in $\\frac{L_p}{d_p}$, recommended to be ≧160 by the Colorado Department of Public Health and Environment as in page 16 of this [Baffling Factor Guidance Manual](https://www.colorado.gov/pacific/sites/default/files/CDPHE%20Baffling%20Factor%20Guidance%20Manual.pdf)\n", - "- Q and v are the volumetric flow rate and velocity of the influent stream, respectively" - ] - }, - { - "cell_type": "markdown", - "id": "13b2bd02", - "metadata": {}, - "source": [ - "Solve for $d_p$:\n", - "\n", - "$$\n", - "d_p [m] = (\\frac{4T_{DT}*Q}{\\pi*AS})^{1/3}\n", - "$$" - ] - }, - { - "cell_type": "markdown", - "id": "e28c8246", - "metadata": {}, - "source": [ - "Then we can calculate the amount of material needed:\n", - "\n", - "$$\n", - "V_{PVC} [m^3] = \\pi * L_p * ((\\frac{d_p}{2}+t_{pipe})^2 - d_p^2)\n", - "$$\n", - "\n", - "where $t_{pipe}$ is the thickness of the pipe." - ] - }, - { - "cell_type": "markdown", - "id": "a903a2e1", - "metadata": {}, - "source": [ - "### 1.2. Chlorine tank" - ] - }, - { - "cell_type": "markdown", - "id": "01ed4c90", - "metadata": {}, - "source": [ - "A cylindrical tank will be used for the storage of the NaOCl solution. For a certain refill inteval $t_{refill}$, volume of the storage tank for 15 wt% NaOCl solution will be:\n", - " \n", - "$$\n", - "V_{NaOCl_{sol}}[m^3] = \\frac{M_{Cl_2}[\\frac{kg}{hr}]*t_{refill}[day]*24[\\frac{hr}{day}]*\\frac{MW_{NaOCl}}{MW_{Cl_2}}}{0.15*\\rho_{sol}[\\frac{kg}{m^3}]}\n", - "$$\n", - " \n", - "where:\n", - "- $M_{Cl_2}$ is the mass flowrate of $Cl_2$ (can be calculated from $C_{res}$, refer to Section 2.1.)\n", - "- $MW_{Cl_2}$ and $MW_{NaOCl}$ are the molar mass of $Cl_2$ (70.91 $\\frac{g}{mol}$) and NaOCl (74.44 $\\frac{g}{mol}$), respectively\n", - "- $\\rho_{sol}$ is the density of a 15% NaOCl solution (1200 $\\frac{kg}{m^3}$)" - ] - }, - { - "cell_type": "markdown", - "id": "a585fe1b", - "metadata": {}, - "source": [ - "Given that\n", - "\n", - "$$\n", - "V_{NaOCl_{sol}} = \\frac{\\pi}{4}d_{cyl}^2*h_{cyl} = \\frac{\\pi}{4}d_{cyl}^2*AS*d_{cyl} = \\frac{\\pi}{2}d_{cyl}^3\n", - "$$\n", - "\n", - "The diameter of the cylinder tank needed to hold this volume is:\n", - " \n", - "$$\n", - "d_{cyl} = \\sqrt[3]{\\frac{2*V_{NaOCl_{sol}}}{\\pi}}\n", - "$$\n", - "\n", - "The corresponding PVC volume is:\n", - "\n", - "$$\n", - "V_{wall} = \\pi*h_{cyl}*((d_{cyl}+2*t_{cyl})^2-d_{cyl}^2) = \\pi*AS*d_{cyl}*((d_{cyl}+2*t_{cyl})^2-d_{cyl}^2)\n", - "$$\n", - "\n", - "$$\n", - "V_{floor} = \\pi*(d_{cyl}+2*t_{cyl})^2*t_{cyl}\n", - "$$\n", - "\n", - "$$\n", - "V_{PVC} [m^3] = V_{wall}+V_{floor}\n", - "$$\n", - "\n", - "where:\n", - "- $h_{cyl}$, $d_{cyl}$, and $t_{cyl}$ are the height, inner diameter, and wall thickness of the cylindrical tank (all in m), respectively\n", - "- AS is the aspect ratio as in $\\frac{h_cyl}{d_cyl}$" - ] - }, - { - "cell_type": "markdown", - "id": "73664ea6", - "metadata": {}, - "source": [ - "### 1.3. Pumps" - ] - }, - { - "cell_type": "markdown", - "id": "564e9603", - "metadata": {}, - "source": [ - "For the design of the pumps, we will use the general algorithms in the `WWTpump` class in `QSDsan` (despite of the name, the pump algorithms are not limited to wastewater treatment settings)." - ] - }, - { - "cell_type": "markdown", - "id": "3954fc5f", - "metadata": {}, - "source": [ - "## 2. Process Algorithms " - ] - }, - { - "cell_type": "markdown", - "id": "fcd1a1e5", - "metadata": {}, - "source": [ - "### 2.1. Chlorine dose" - ] - }, - { - "cell_type": "markdown", - "id": "c02f398c", - "metadata": {}, - "source": [ - "Based on the following equation to take into account the amount of chlorine lost to reactions with organics (quantified as the total organic carbon, TOC and ultraviolet absorbance, UVA), we can back-calculate $C_0$ using $C_{res}$:\n", - "\n", - "$$\n", - "C_{res} = -0.8404C_0*ln\\frac{C_0}{C_{res}} - 0.404TOC [\\frac{mg_{C}}{L}]*T_{contact}*(\\frac{C_0}{UVA [1/cm]})^{-0.9108} + C_0\n", - "$$" - ] - }, - { - "cell_type": "markdown", - "id": "5f38c936", - "metadata": {}, - "source": [ - "\n", - " \n", - "- Q1: I'm not sure how TOC and UVA are quantified (e.g., units for them in the equation above)? \n", - "- Q2: Are there two solutions of $C_0$ at a certain $C_{res}$? If so, we probably would want to use the lower value.\n", - " - We might want to double-check the results get from ``scipy`` vs. ``flexsolve``\n", - " \n", - "" - ] - }, - { - "cell_type": "markdown", - "id": "c5a87356", - "metadata": {}, - "source": [ - "With $C_0$ solved, we will know how much NaOCl we need to add to achieve the desired CT:\n", - "\n", - "$$\n", - "M_{Cl_2}[\\frac{kg}{hr}] = Q[\\frac{m^3}{hr}]*C_0[\\frac{g}{m^3}]*\\frac{1[kg]}{1000[g]}\n", - "$$\n", - "\n", - "where:\n", - "- $M_{Cl_2}$ is the mass flowrate of $Cl_2$\n", - "- $Q$ is the volumetric flowrate of the influent" - ] - }, - { - "cell_type": "markdown", - "id": "69b8d85f", - "metadata": {}, - "source": [ - "### 2.2. Pumping energy" - ] - }, - { - "cell_type": "markdown", - "id": "87c78ea5", - "metadata": {}, - "source": [ - "Pumping energy can be calculated based on the flow rate and head pressure/loss as:\n", - "\n", - "$$\n", - "P [kW] = \\frac{mgH}{1000\\eta}\n", - "$$\n", - "\n", - "where:\n", - "- $m$ is mass flow rate in $[\\frac{kg}{s}]$\n", - "- $H$ is the head pressure/loss $[m]$\n", - "- $\\eta$ is the typical pump efficiency (set to 60%)" - ] - }, - { - "cell_type": "markdown", - "id": "a6659f39", - "metadata": {}, - "source": [ - "#### 2.2.1. For the contact zone" - ] - }, - { - "cell_type": "markdown", - "id": "6a7a1048", - "metadata": {}, - "source": [ - "In the case of serpentine tubing, head loss is the sum of the major head loss ($H_f$; due to friction) and minor head loss ($H_m$; due to bends in flow):\n", - "\n", - "$$\n", - "H [m] = H_f + H_m\n", - "$$" - ] - }, - { - "cell_type": "markdown", - "id": "2996a35b", - "metadata": {}, - "source": [ - "For the major head loss, the [Hazen-Williams equation](https://en.wikipedia.org/wiki/Hazen%E2%80%93Williams_equation) can be used (coefficients from [here](https://www.engineeringtoolbox.com/hazen-williams-water-d_797.html)):\n", - "\n", - "$$\n", - "H_f = \\frac{0.2083*(\\frac{100*Q}{C})^{1.852}}{100*d_p^{4.8655}} * L_p\n", - "$$\n", - "\n", - "where C is the roughness coefficient and assumed to be 150 for PVC." - ] - }, - { - "cell_type": "markdown", - "id": "7d9d7b46", - "metadata": {}, - "source": [ - "The minor head loss can be calculated as:\n", - "\n", - "$$\n", - "H_m = \\frac{\\epsilon*v^2}{2g} * N_{bend}\n", - "$$\n", - "\n", - "where:\n", - "- $\\epsilon$ is the minor loss coefficient and assumed to be 1.5\n", - "- $N_{bend}$ is the number of bends can be calculated by dividing the total length by the segment length\n", - "\n", - "$N_{bend}$ can be calculated as\n", - "\n", - "$$\n", - "N_{bend} = \\frac{L_p}{L_{seg}}\n", - "$$\n", - "\n", - "and the segment length $L_{seg}$ can be calculated based on the segment length-to-diameter ratio (recommended to be ≦40 by the Colorado Department of Public Health and Environment as in page 15 of this [Baffling Factor Guidance Manual](https://www.colorado.gov/pacific/sites/default/files/CDPHE%20Baffling%20Factor%20Guidance%20Manual.pdf))." - ] - }, - { - "cell_type": "markdown", - "id": "9f0a999c", - "metadata": {}, - "source": [ - "#### 2.2.2. For the storage tank" - ] - }, - { - "cell_type": "markdown", - "id": "aa0e0f0c", - "metadata": {}, - "source": [ - "For the cylindrical storage tank, there is no minor head loss, therefore the total head loss only comes from the friction loss. However, we need to consider head pressure needed for clorine addition, which is assumed to be 70.3 m. Therefore, the total head needed is\n", - "\n", - "$$\n", - "H = H_f + H_p = \\frac{0.2083*(\\frac{100*Q}{C})^{1.852}}{100*d_{cyl}^{4.8655}} * h_{cyl} + 70.3\n", - "$$" - ] - }, - { - "cell_type": "markdown", - "id": "2f3ff298", - "metadata": {}, - "source": [ - "[Back to top](#top)" - ] - }, - { - "cell_type": "markdown", - "id": "b6a928f2", - "metadata": {}, - "source": [ - "## 3. Unit Classes " - ] - }, - { - "cell_type": "markdown", - "id": "d5691410", - "metadata": {}, - "source": [ - "### 3.1. Contact zone" - ] - }, - { - "cell_type": "markdown", - "id": "bbe29d99", - "metadata": {}, - "source": [ - "For the contact zone, we need to create a new class. Check out the tutorials on `SanUnit` ([basic](https://qsdsan.readthedocs.io/en/latest/tutorials/4_SanUnit_basic.html), [advanced](https://qsdsan.readthedocs.io/en/latest/tutorials/5_SanUnit_advanced.html)) for how to make a new `SanUnit` subclass." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "2081965a", - "metadata": {}, - "outputs": [], - "source": [ - "from warnings import warn\n", - "from math import log, pi, ceil\n", - "from flexsolve import IQ_interpolation\n", - "from qsdsan import SanUnit, Construction\n", - "from qsdsan.sanunits import WWTpump\n", - "\n", - "class ContactZone(SanUnit):\n", - " '''\n", - " Contact zone for water disinfection using chlorine (in the form of sodium hypochlorite, NaOCl).\n", - "\n", - " Parameters\n", - " ----------\n", - " ins : Iterable(obj)\n", - " Influent stream, NaOCl (updated upon unit simulation).\n", - " outs : obj\n", - " Disinfected stream.\n", - " target_CT : float\n", - " Desired CT (concentration*time) for microorganism in min-mg/L.\n", - " C_res : float\n", - " Desired residual concentration of disinfectant in mg/L.\n", - " UVA : float\n", - " Disinfection credit from UVA.\n", - " PVC_thickness : float\n", - " Thickness of the PVC material in m.\n", - "\n", - " References\n", - " ----------\n", - " [1] Jones et al., Life cycle environmental impacts of disinfection technologies\n", - " used in small drinking water systems.\n", - " Environmental Science & Technology, 2018, 52 (5), 2998-3007.\n", - " https://doi.org/10.1021/acs.est.7b04448\n", - " [2] Disinfection Profiling and Benchmarking Technical Guidance Manual.\n", - " U.S. Environmental Protection Agency.\n", - " https://www.epa.gov/system/files/documents/2022-02/disprof_bench_3rules_final_508.pdf\n", - " [3] Baffling Factor Guidance Manual.\n", - " Colorado Department of Public Health and Environment.\n", - " https://www.colorado.gov/pacific/sites/default/files/CDPHE%20Baffling%20Factor%20Guidance%20Manual.pdf\n", - "\n", - " Examples\n", - " --------\n", - " Here we will skip this as we will show how to use it later.\n", - " '''\n", - "\n", - " _N_ins = 2 # influent stream, NaOCl solution\n", - " _N_outs = 1 # disinfected water\n", - " baffling_factor = 0.7\n", - " aspect_ratio = 160 # length over diamteter\n", - " segment_L_to_dia = 40 # segment length to diameter ratio\n", - " C = 150 # roughness coefficient\n", - " epsilon = 1.5 # minor loss coefficient\n", - " pump_eff = 0.6\n", - "\n", - " def __init__(self, ID='', ins=None, outs=(), thermo=None, init_with='WasteStream',\n", - " target_CT=4, # based on the table, set default at T=15°C and pH=6-9\n", - " C_res=10, UVA=1, #!!! need to update\n", - " PVC_thickness=0.005,\n", - " **kwargs):\n", - " SanUnit.__init__(self, ID, ins, outs, thermo, init_with)\n", - " self.target_CT = target_CT\n", - " self.C_res= C_res\n", - " self.UVA = UVA\n", - " self.PVC_thickness = PVC_thickness\n", - " for attr, val in kwargs: setattr(self, kwargs)\n", - "\n", - " # To consider LCA impacts from the construction material\n", - " self.construction = (\n", - " Construction('ContactZone_PVC', linked_unit=self,\n", - " item='PVC', quantity_unit='kg'),\n", - " Construction('ContactZone_SS', linked_unit=self,\n", - " item='StainlessSteel', quantity_unit='kg'),\n", - " )\n", - "\n", - " # Pump\n", - " ID = self.ID\n", - " eff = self.outs[0]\n", - " self.pump = WWTpump(\n", - " ID=ID+'_pump', ins=eff.proxy(eff.ID+'_proxy'),\n", - " pump_type='', # use the generic pump algorithm\n", - " N_pump=1, capacity_factor=1, include_pump_cost=True,\n", - " include_building_cost=False, include_OM_cost=False,\n", - " )\n", - "\n", - " # Target function to solve C_0 -->\n", - " #i first thought the found result was not matching another solver,\n", - " # however the equation just has 2 solutions.\n", - " # We'll probably have to discuss with Eva, which solution to use.\n", - " @staticmethod\n", - " def _C_res_at_C_0(C_0, TOC, contact_time, UVA, C_res):\n", - " C_res2 = -0.8404*C_0*log(C_0/C_res) - 0.404*TOC*contact_time*(C_0/UVA)**(-0.9108) + C_0\n", - " return C_res2-C_res\n", - "\n", - " # Implement process algorithms\n", - " def _run(self):\n", - " inf, naocl = self.ins\n", - " eff, = self.outs\n", - "\n", - " # Calculate contact time and C_0\n", - " TOC = inf.TOC # in mg/L\n", - " UVA = self.UVA\n", - " C_res = self.C_res\n", - " contact_time = self.target_CT / self.C_res\n", - " try:\n", - " C_0 = IQ_interpolation( # in mg/L\n", - " f=self._C_res_at_C_0, x0=C_res, x1=100*C_res, # assume that C_0 won't be >100X of C_res\n", - " ytol=1e-6, args=(TOC, contact_time, UVA, C_res),\n", - " checkbounds=False)\n", - " except:\n", - " warn('Could not find C_0 for the specified values of TOC, contact_time, UVA and C_res.'\n", - " 'C_0 is assumed to be the same as C_res, resullts may be faulty!')\n", - " C_0 = C_res # assumed\n", - "\n", - " C_naocl = C_0/70.91*74.44 # 1-to-1 molar conversion of C_0 (for Cl2) to NaOCl\n", - " naocl.imass['NaOCl'] = m_naocl = inf.F_vol * C_naocl / 1000 # m3*mg/L/1000 = kg\n", - " naocl.imass['Water'] = m_naocl/0.15 - m_naocl\n", - "\n", - " eff.mix_from(self.ins)\n", - " eff.imass['NaOCl'] *= C_res/C_0 # account for the consumed NaOCl\n", - "\n", - " _units = { # units of measure for the design parameters\n", - " 'Pipe diameter': 'm',\n", - " 'Pipe length': 'm',\n", - " 'Total PVC': 'm3',\n", - " 'Pump head': 'm',\n", - " 'Pump stainless steel': 'kg',\n", - " }\n", - "\n", - " # Implement design algorithms\n", - " def _design(self):\n", - " D = self.design_results\n", - "\n", - " # Pipe dimensions\n", - " contact_time=self.target_CT / self.C_res\n", - " t_DT = contact_time / self.baffling_factor # theoretical detention time\n", - " Q = self.F_vol_in # m3/hr\n", - " t_PVC, AS, C = self.PVC_thickness, self.aspect_ratio, self.C\n", - " dia = (4*t_DT*Q/(pi*AS))**(1/3)\n", - " dia_out = dia + 2*t_PVC\n", - " D['Pipe diameter'] = dia\n", - " L_p = D['Pipe length'] = dia * AS\n", - " V_PVC = D['Total PVC'] = pi * L_p * ((dia_out/2)**2-dia**2)\n", - "\n", - " # Pump head\n", - " H_f = 0.2083*(100*Q/C)**1.852/(100*dia**4.8655)*L_p # m\n", - " v = Q/(pi*dia**2)\n", - " N_bend = ceil(L_p/(dia*self.segment_L_to_dia))\n", - " H_m = self.epsilon*v**2/(2*9.81)*N_bend\n", - " H = D['Pump head'] = H_f + H_m\n", - "\n", - " # Pump\n", - " pump = self.pump\n", - " pump.simulate()\n", - " m_ss = D['Pump stainless steel'] = pump.design_results['Pump stainless steel']\n", - " self.power_utility.rate = self.F_mass_in*9.81*H/(1000*self.pump_eff)\n", - "\n", - " #!!! Will need CAPEX/OPEX/impacts of the UVA lights as well\n", - "\n", - " # Construction materials for TEA/LCA\n", - " self.construction[0].quantity = V_PVC\n", - " self.construction[1].quantity = m_ss\n", - " self.add_construction(add_cost=True) # this will add PVC and SS cost\n", - "\n", - " _F_BM_default = {\n", - " 'PVC': 1,\n", - " 'StainlessSteel': 1,\n", - " 'Pump': 1.18*(1+0.007/100),\n", - " }\n", - " def _cost(self):\n", - " C = self.baseline_purchase_costs\n", - " C['Pump'] = self.pump.baseline_purchase_costs['Pump']" - ] - }, - { - "cell_type": "markdown", - "id": "d336dead", - "metadata": {}, - "source": [ - "### 3.2. ChlorineTank" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "1af670fd", - "metadata": {}, - "outputs": [], - "source": [ - "from qsdsan.sanunits import MixTank\n", - "\n", - "class ChlorineTank(MixTank):\n", - " '''\n", - " A subclass of `MixTank` with an auxiliary pump for chlorine storage.\n", - "\n", - " Parameters\n", - " ----------\n", - " ins : Iterable(obj)\n", - " NaOCl, water.\n", - " outs : obj\n", - " NaOCl solution.\n", - " t_refill : float\n", - " Tank refill interval in d.\n", - " head_pressure : float\n", - " Assumed head pressure for the pump in m.\n", - " PVC_thickness : float\n", - " Thickness of the PVC material in m.\n", - "\n", - " See Also\n", - " --------\n", - " `qsdsan.sanunits.MixTank `_\n", - " '''\n", - "\n", - " aspect_ratio = 2 # height over diameter\n", - " C = 150 # roughness coefficient\n", - " pump_eff = 0.6\n", - "\n", - " def __init__(self, ID='', ins=None, outs=(), thermo=None, init_with='WasteStream',\n", - " t_refill=7, head_pressure=70.3, PVC_thickness=0.02, **kwargs):\n", - " MixTank.__init__(self, ID, ins, outs, thermo)\n", - " self.head_pressure = head_pressure\n", - " self.PVC_thickness = PVC_thickness\n", - " for attr, val in kwargs: setattr(self, kwargs)\n", - "\n", - " # To consider LCA impacts from the construction material\n", - " self.construction = (\n", - " Construction('ChlorineTank_PVC', linked_unit=self,\n", - " item='PVC', quantity_unit='kg'),\n", - " Construction('ChlorineTank_SS', linked_unit=self,\n", - " item='StainlessSteel', quantity_unit='kg'),\n", - " )\n", - " eff = self.outs[0]\n", - " self.pump = WWTpump(\n", - " ID=self.ID+'_pump', ins=eff.proxy(eff.ID+'_proxy'),\n", - " pump_type='', # use the generic pump algorithm\n", - " N_pump=1, capacity_factor=1, include_pump_cost=True,\n", - " include_building_cost=False, include_OM_cost=False,\n", - " )\n", - "\n", - " def _run(self):\n", - " naocl, water = self.ins # NaOCl dose will be adjusted when assesmbling the system\n", - " eff = self.outs[0]\n", - " naocl.copy_flow(eff, IDs=('NaOCl',))\n", - " water.copy_flow(eff, IDs=('Water',))\n", - "\n", - "\n", - " _units = { # units of measure for the design parameters\n", - " 'Tank diameter': 'm',\n", - " 'Tank height': 'm',\n", - " 'Total PVC': 'm3',\n", - " 'Pump head': 'm',\n", - " 'Pump stainless steel': 'kg',\n", - " }\n", - " def _design(self):\n", - " MixTank._design(self)\n", - " D = self.design_results\n", - " eff = self.outs[0]\n", - "\n", - " # Cylindrical tank\n", - " V_naocl = self.ins[0].F_vol #!!! the simulated density is ~1.1 g/mL, want to use 1.2?\n", - " AS, t_PVC = self.aspect_ratio, self.PVC_thickness\n", - " dia = 2*((V_naocl/(pi*AS))**(1/3))\n", - " dia_out = dia + 2*self.PVC_thickness\n", - "\n", - " D['Tank diameter'] = dia\n", - " h_cyl = D['Tank height'] = dia * AS\n", - " V_wall = pi*h_cyl*(dia_out**2-dia**2)\n", - " V_floor = pi * dia_out**2 * t_PVC\n", - " V_PVC = D['Total PVC'] = V_wall + V_floor\n", - "\n", - " # Pump\n", - " Q = eff.F_vol\n", - " C = self.C\n", - " pump = self.pump\n", - " H_f = 0.2083*(100*Q/C)**1.852/(100*dia**4.8655) * h_cyl # m\n", - " H_p = self.head_pressure\n", - " D['Pump head'] = H_f + H_p\n", - " pump.simulate()\n", - " m_ss = D['Pump stainless steel'] = pump.design_results['Pump stainless steel']\n", - "\n", - " # # This is if want to use the default algorithms for calculating electricity usage,\n", - " # # it's more conservative (i.e., the efficiency is lower)\n", - " # pump._H_f = H_f * 3.28 # ft\n", - " # pump._H_p = H_p * 3.28 # ft\n", - "\n", - " # Construction materials for TEA/LCA\n", - " self.construction[0].quantity = V_PVC\n", - " self.construction[1].quantity = m_ss\n", - " self.add_construction(add_cost=True) # this will add PVC and SS cost\n", - "\n", - " _F_BM_default = {\n", - " 'PVC': 1,\n", - " 'StainlessSteel': 1,\n", - " 'Pump': 1.18*(1+0.007/100),\n", - " }\n", - " def _cost(self):\n", - " MixTank._cost(self) #!!! this will also add the cost for a stainless steel tank\n", - " pump = self.pump\n", - " self.baseline_purchase_costs['Pump'] = pump.baseline_purchase_costs['Pump']\n", - " H = self.design_results['Pump head']\n", - " self.power_utility.rate += self.F_mass_in*9.81*H/(1000*self.pump_eff)" - ] - }, - { - "cell_type": "markdown", - "id": "476ac60d", - "metadata": {}, - "source": [ - "\n", - " \n", - "- Q3: Density of the simulated NaOCl solution is ~1.1 g/mL instead of 1.2, which will make the design more conservative (since volume is larger), do we want to stick to the 1.2?\n", - " - Related, when calculating tank volume, we typically considers a \"working volume\" factor (<1, our default is 0.8) since we don't want to fill the tank 100% full. So the actual volume will be $\\frac{V_{calculated}}{factor}$, do we want to do the same for this storage tank?\n", - " \n", - "" - ] - }, - { - "cell_type": "markdown", - "id": "62d9849e", - "metadata": {}, - "source": [ - "## 4. System, TEA, and LCA " - ] - }, - { - "cell_type": "markdown", - "id": "650a5f5c", - "metadata": {}, - "source": [ - "Finally it's time to create and simulate the entire system." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "68423ea8", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/yalinli_cabbi/Library/CloudStorage/OneDrive-Personal/Coding/bst/biosteam/_unit.py:635: RuntimeWarning: the purchase cost item, 'Tanks', has no defined bare-module factor in the 'ChlorineTank.F_BM' dictionary; bare-module factor now has a default value of 1\n", - " warn(warning)\n" - ] - } - ], - "source": [ - "# Identify the components needed for simulation\n", - "import qsdsan as qs\n", - "from qsdsan import Component, Components, set_thermo, WasteStream, \\\n", - " System, SimpleTEA, ImpactIndicator, ImpactItem, StreamImpactItem, LCA\n", - "\n", - "# Set up components to be used in simulation\n", - "kwargs = {\n", - " 'phase': 'l',\n", - " 'particle_size': 'Soluble',\n", - " 'degradability': 'Undegradable',\n", - " 'organic': False,\n", - "}\n", - "H2O = Component('H2O', **kwargs)\n", - "\n", - "kwargs['phase'] = 's'\n", - "kwargs['particle_size'] = 'Particulate'\n", - "NaOCl = Component('NaOCl', **kwargs)\n", - "NaOCl.copy_models_from(qs.Component('HOCl', **kwargs), ['V']) # this gives a rho of ~1.1 g/mL for 15 wt% solution\n", - "\n", - "cmps = Components([H2O, NaOCl])\n", - "cmps.compile()\n", - "cmps.set_alias('H2O', 'Water')\n", - "set_thermo(cmps)\n", - "\n", - "# # Redundant codes, remove after module done\n", - "# HCl = Component('HCl', **kwargs)\n", - "# HOCl = Component('HOCl', **kwargs)\n", - "# NH3 = Component('NH3', **kwargs) # assumed to be liquefied NH3\n", - "# cmps = Components([H2O, NaOCl, HCl, HOCl, NH3])\n", - "# cmps.set_alias('NH3', 'Ammonia')\n", - "# s = WasteStream(Water=85, NaOCl=15, units='kg/hr')\n", - "\n", - "\n", - "# Impact items for LCA, values all made-up now\n", - "GWP = ImpactIndicator('GWP', unit='kg CO2')\n", - "PVC = ImpactItem('PVC', GWP=1, price=1)\n", - "StainlessSteel = ImpactItem('StainlessSteel', GWP=5, price=5)\n", - "NaOCl_item = StreamImpactItem('naocl_item', GWP=2)\n", - "e_item = ImpactItem('e_item', functional_unit='kWh', GWP=1.1)\n", - "\n", - "# Streams\n", - "influent = WasteStream('influent', Water=100, units='kg/hr') # an assumed fake stream\n", - "naocl = WasteStream('naocl', price=1, stream_impact_item=NaOCl_item, units='kg/hr') # price is made-up\n", - "water = WasteStream('water')\n", - "disinfected = WasteStream('disinfected')\n", - "\n", - "U1 = ContactZone('U1', ins=(influent, 'naocl_solution'), outs=disinfected)\n", - "U2 = ChlorineTank('U2', ins=(naocl, water), outs=1-U1)\n", - "\n", - "sys = System('sys', path=(U1, U2))\n", - "sys.simulate()\n", - "\n", - "tea = SimpleTEA(sys, discount_rate=0.5, income_tax=0.3, lifetime=10)\n", - "\n", - "get_e_item_quantity = lambda: (sys.get_electricity_consumption()-sys.get_electricity_production())*tea.lifetime\n", - "lca = LCA(sys, lifetime=tea.lifetime, e_item=get_e_item_quantity)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "2fd30f57", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "450.8329582699148" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# TEA results\n", - "def get_price():\n", - " price = tea.solve_price(disinfected)\n", - " price = price*disinfected.F_mass/disinfected.F_vol # per m3\n", - " return price\n", - "\n", - "get_price()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "72440dfe", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "37102.10326916433" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# LCA results\n", - "def get_impact():\n", - " impact = lca.get_total_impacts(time=1)['GWP'] # per hour\n", - " impact = impact/disinfected.F_vol # per m3\n", - " return impact\n", - "\n", - "get_impact()" - ] - }, - { - "cell_type": "markdown", - "id": "8bdf18d1", - "metadata": {}, - "source": [ - "\n", - " \n", - "We need the following data for TEA/LCA (below are ones I can think of now, there might be more)\n", - "- Lifetime of the equipment and TEA/LCA\n", - "- TEA\n", - " - Costs of the unit. If we don't have the cost for the entire unit, we can calculate based on the materials (and we would need the unit costs of PVC/stainless steel), as well as the UVA lights\n", - " - Costs of NaOCl (pure vs. solution?)\n", - " - Electricity usage of UVA lights\n", - " - Other assumptions like discount rate, income tax, etc.\n", - "- LCA\n", - " - Life cycle inventory assessment method (e.g., ReCiPe) with the corresponding characterization factors for materials (PVC, stainless steel, UVA lights), chemicals (NaOCl), and electricity. I only used GWP here as an example, we can do any number of LCIA methods/indicators you like.\n", - " \n", - "" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "33c60ab6", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a83ecc29", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "df2274a3", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "257b7c8a", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a592d8a3", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7e7599b4", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "275115c2", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "9d9a485a", - "metadata": {}, - "source": [ - "[Back to top](#top)" - ] - } - ], - "metadata": { - "interpreter": { - "hash": "5c4384bbfe0fafd87c455cafafefa588d87617773c75dc9eb96f43c39a856362" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/source/tutorials/1_Helpful_Basics.ipynb b/docs/source/tutorials/1_Helpful_Basics.ipynb index 81bc154b..1f4b8afa 100644 --- a/docs/source/tutorials/1_Helpful_Basics.ipynb +++ b/docs/source/tutorials/1_Helpful_Basics.ipynb @@ -9,8 +9,8 @@ "\n", "- **Prepared by:**\n", " \n", - " - [Yalin Li](https://qsdsan.readthedocs.io/en/latest/authors/Yalin_Li.html)\n", - " - [Joy Zhang](https://qsdsan.readthedocs.io/en/latest/authors/Joy_Zhang.html)\n", + " - [Yalin Li](https://qsdsan.readthedocs.io/en/latest/CONTRIBUTING.html)\n", + " - [Joy Zhang](https://qsdsan.readthedocs.io/en/latest/CONTRIBUTING.html)\n", " \n", "- **Covered topics:**\n", "\n", @@ -19,9 +19,9 @@ " \n", "- **Video demo:**\n", "\n", - " - [Yalin Li](https://qsdsan.readthedocs.io/en/latest/authors/Yalin_Li.html)\n", + " - [Yalin Li](https://qsdsan.readthedocs.io/en/latest/CONTRIBUTING.html)\n", " \n", - "To run tutorials in your browser, go to this [Binder page](https://mybinder.org/v2/gh/QSD-Group/QSDsan/main?filepath=%2Fdocs%2Fsource%2Ftutorials).\n", + "To run tutorials in your browser, go to this [Binder page](https://mybinder.org/v2/gh/QSD-Group/QSDsan-env/main?urlpath=git-pull%3Frepo%3Dhttps%253A%252F%252Fgithub.com%252FQSD-group%252FQSDsan%26urlpath%3Dtree%252FQSDsan%252Fdocs%252Fsource%252Ftutorials%26branch%3Dmain).\n", " \n", "You can also watch a video demo on [YouTube](https://www.youtube.com/watch?v=g8mXWycdi4E) (subscriptions & likes appreciated!)." ] @@ -690,7 +690,36 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.9.13" + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false } }, "nbformat": 4, diff --git a/docs/source/tutorials/2_Component.ipynb b/docs/source/tutorials/2_Component.ipynb index 3e667b23..078db8c2 100644 --- a/docs/source/tutorials/2_Component.ipynb +++ b/docs/source/tutorials/2_Component.ipynb @@ -8,8 +8,8 @@ "\n", "- **Prepared by:**\n", " \n", - " - [Yalin Li](https://qsdsan.readthedocs.io/en/latest/authors/Yalin_Li.html)\n", - " - [Joy Zhang](https://qsdsan.readthedocs.io/en/latest/authors/Joy_Zhang.html)\n", + " - [Yalin Li](https://qsdsan.readthedocs.io/en/latest/CONTRIBUTING.html)\n", + " - [Joy Zhang](https://qsdsan.readthedocs.io/en/latest/CONTRIBUTING.html)\n", "\n", "- **Covered topics:**\n", "\n", @@ -22,7 +22,7 @@ "\n", " - [Tori Morgan](https://qsdsan.readthedocs.io/en/latest/authors/Tori_Morgan.html)\n", " \n", - "To run tutorials in your browser, go to this [Binder page](https://mybinder.org/v2/gh/QSD-Group/QSDsan/main?filepath=%2Fdocs%2Fsource%2Ftutorials).\n", + "To run tutorials in your browser, go to this [Binder page](https://mybinder.org/v2/gh/QSD-Group/QSDsan-env/main?urlpath=git-pull%3Frepo%3Dhttps%253A%252F%252Fgithub.com%252FQSD-group%252FQSDsan%26urlpath%3Dtree%252FQSDsan%252Fdocs%252Fsource%252Ftutorials%26branch%3Dmain).\n", "\n", "You can also watch a video demo on [YouTube](https://www.youtube.com/watch?v=1OlGsjbqUX8) (subscriptions & likes appreciated!)." ] @@ -1575,6 +1575,35 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.13" + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false } }, "nbformat": 4, diff --git a/docs/source/tutorials/3_WasteStream.ipynb b/docs/source/tutorials/3_WasteStream.ipynb index 6fb337ee..8434a853 100644 --- a/docs/source/tutorials/3_WasteStream.ipynb +++ b/docs/source/tutorials/3_WasteStream.ipynb @@ -8,8 +8,8 @@ "\n", "- **Prepared by:**\n", " \n", - " - [Yalin Li](https://qsdsan.readthedocs.io/en/latest/authors/Yalin_Li.html)\n", - " - [Joy Zhang](https://qsdsan.readthedocs.io/en/latest/authors/Joy_Zhang.html)\n", + " - [Yalin Li](https://qsdsan.readthedocs.io/en/latest/CONTRIBUTING.html)\n", + " - [Joy Zhang](https://qsdsan.readthedocs.io/en/latest/CONTRIBUTING.html)\n", "\n", "- **Covered topics:**\n", "\n", @@ -20,7 +20,7 @@ "\n", " - [Hannah Lohman](https://qsdsan.readthedocs.io/en/latest/authors/Hannah_Lohman.html)\n", " \n", - "To run tutorials in your browser, go to this [Binder page](https://mybinder.org/v2/gh/QSD-Group/QSDsan/main?filepath=%2Fdocs%2Fsource%2Ftutorials).\n", + "To run tutorials in your browser, go to this [Binder page](https://mybinder.org/v2/gh/QSD-Group/QSDsan-env/main?urlpath=git-pull%3Frepo%3Dhttps%253A%252F%252Fgithub.com%252FQSD-group%252FQSDsan%26urlpath%3Dtree%252FQSDsan%252Fdocs%252Fsource%252Ftutorials%26branch%3Dmain).\n", "\n", "You can also watch a video demo on [YouTube](https://youtu.be/yCOZ0F6E1Sw) (subscriptions & likes appreciated!)." ] @@ -1012,7 +1012,36 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.9.13" + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false } }, "nbformat": 4, diff --git a/docs/source/tutorials/4_SanUnit_basic.ipynb b/docs/source/tutorials/4_SanUnit_basic.ipynb index 6eaf6216..7837358c 100644 --- a/docs/source/tutorials/4_SanUnit_basic.ipynb +++ b/docs/source/tutorials/4_SanUnit_basic.ipynb @@ -8,7 +8,7 @@ "\n", "- **Prepared by:**\n", " \n", - " - [Yalin Li](https://qsdsan.readthedocs.io/en/latest/authors/Yalin_Li.html)\n", + " - [Yalin Li](https://qsdsan.readthedocs.io/en/latest/CONTRIBUTING.html)\n", "\n", "- **Covered topics:**\n", "\n", @@ -17,9 +17,9 @@ " \n", "- **Video demo:**\n", "\n", - " - [Tori Morgan](https://qsdsan.readthedocs.io/en/latest/authors/Tori_Morgan.html)\n", + " - [Tori Morgan](https://qsdsan.readthedocs.io/en/latest/CONTRIBUTING.html)\n", " \n", - "To run tutorials in your browser, go to this [Binder page](https://mybinder.org/v2/gh/QSD-Group/QSDsan/main?filepath=%2Fdocs%2Fsource%2Ftutorials).\n", + "To run tutorials in your browser, go to this [Binder page](https://mybinder.org/v2/gh/QSD-Group/QSDsan-env/main?urlpath=git-pull%3Frepo%3Dhttps%253A%252F%252Fgithub.com%252FQSD-group%252FQSDsan%26urlpath%3Dtree%252FQSDsan%252Fdocs%252Fsource%252Ftutorials%26branch%3Dmain).\n", "\n", "You can also watch a video demo on [YouTube](https://youtu.be/s9zr0rCX3UY) (subscriptions & likes appreciated!)." ] @@ -1113,6 +1113,35 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.13" + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false } }, "nbformat": 4, diff --git a/docs/source/tutorials/5_SanUnit_advanced.ipynb b/docs/source/tutorials/5_SanUnit_advanced.ipynb index 4ba7fdf3..7c0a4179 100644 --- a/docs/source/tutorials/5_SanUnit_advanced.ipynb +++ b/docs/source/tutorials/5_SanUnit_advanced.ipynb @@ -8,7 +8,7 @@ "\n", "- **Prepared by:**\n", " \n", - " - [Yalin Li](https://qsdsan.readthedocs.io/en/latest/authors/Yalin_Li.html)\n", + " - [Yalin Li](https://qsdsan.readthedocs.io/en/latest/CONTRIBUTING.html)\n", "\n", "- **Covered topics:**\n", "\n", @@ -19,9 +19,9 @@ " \n", "- **Video demo:**\n", "\n", - " - [Hannah Lohman](https://qsdsan.readthedocs.io/en/latest/authors/Hannah_Lohman.html)\n", + " - [Hannah Lohman](https://qsdsan.readthedocs.io/en/latest/CONTRIBUTING.html)\n", " \n", - "To run tutorials in your browser, go to this [Binder page](https://mybinder.org/v2/gh/QSD-Group/QSDsan/main?filepath=%2Fdocs%2Fsource%2Ftutorials).\n", + "To run tutorials in your browser, go to this [Binder page](https://mybinder.org/v2/gh/QSD-Group/QSDsan-env/main?urlpath=git-pull%3Frepo%3Dhttps%253A%252F%252Fgithub.com%252FQSD-group%252FQSDsan%26urlpath%3Dtree%252FQSDsan%252Fdocs%252Fsource%252Ftutorials%26branch%3Dmain).\n", "\n", "You can also watch a video demo on [YouTube](https://youtu.be/G20J2U8g7Dg) (subscriptions & likes appreciated!)." ] @@ -1614,6 +1614,35 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.13" + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false } }, "nbformat": 4, diff --git a/docs/source/tutorials/6_System.ipynb b/docs/source/tutorials/6_System.ipynb index 875fb145..b95c555a 100644 --- a/docs/source/tutorials/6_System.ipynb +++ b/docs/source/tutorials/6_System.ipynb @@ -8,7 +8,7 @@ "\n", "* **Prepared by:**\n", " \n", - " - [Yalin Li](https://qsdsan.readthedocs.io/en/latest/authors/Yalin_Li.html)\n", + " - [Yalin Li](https://qsdsan.readthedocs.io/en/latest/CONTRIBUTING.html)\n", "\n", "* **Covered topics:**\n", "\n", @@ -17,9 +17,9 @@ "\n", "- **Video demo:**\n", "\n", - " - [Tori Morgan](https://qsdsan.readthedocs.io/en/latest/authors/Tori_Morgan.html)\n", + " - [Tori Morgan](https://qsdsan.readthedocs.io/en/latest/CONTRIBUTING.html)\n", "\n", - "To run tutorials in your browser, go to this [Binder page](https://mybinder.org/v2/gh/QSD-Group/QSDsan/main?filepath=%2Fdocs%2Fsource%2Ftutorials).\n", + "To run tutorials in your browser, go to this [Binder page](https://mybinder.org/v2/gh/QSD-Group/QSDsan-env/main?urlpath=git-pull%3Frepo%3Dhttps%253A%252F%252Fgithub.com%252FQSD-group%252FQSDsan%26urlpath%3Dtree%252FQSDsan%252Fdocs%252Fsource%252Ftutorials%26branch%3Dmain).\n", "\n", "You can also watch a video demo on [YouTube](https://youtu.be/iIx28JkNjQ8) (subscriptions & likes appreciated!)." ] @@ -668,7 +668,36 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.9.13" + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false } }, "nbformat": 4, diff --git a/docs/source/tutorials/7_TEA.ipynb b/docs/source/tutorials/7_TEA.ipynb index 559b9b74..cfcdb544 100755 --- a/docs/source/tutorials/7_TEA.ipynb +++ b/docs/source/tutorials/7_TEA.ipynb @@ -8,7 +8,7 @@ "\n", "* **Prepared by:**\n", " \n", - " - [Yalin Li](https://qsdsan.readthedocs.io/en/latest/authors/Yalin_Li.html)\n", + " - [Yalin Li](https://qsdsan.readthedocs.io/en/latest/CONTRIBUTING.html)\n", "\n", "* **Covered topics:**\n", "\n", @@ -17,9 +17,9 @@ " \n", "- **Video demo:**\n", "\n", - " - [Hannah Lohman](https://qsdsan.readthedocs.io/en/latest/authors/Hannah_Lohman.html)\n", + " - [Hannah Lohman](https://qsdsan.readthedocs.io/en/latest/CONTRIBUTING.html)\n", "\n", - "To run tutorials in your browser, go to this [Binder page](https://mybinder.org/v2/gh/QSD-Group/QSDsan/main?filepath=%2Fdocs%2Fsource%2Ftutorials).\n", + "To run tutorials in your browser, go to this [Binder page](https://mybinder.org/v2/gh/QSD-Group/QSDsan-env/main?urlpath=git-pull%3Frepo%3Dhttps%253A%252F%252Fgithub.com%252FQSD-group%252FQSDsan%26urlpath%3Dtree%252FQSDsan%252Fdocs%252Fsource%252Ftutorials%26branch%3Dmain).\n", "\n", "You can also watch a video demo on [YouTube](https://youtu.be/v3qNNZypTKY) (subscriptions & likes appreciated!)." ] @@ -729,6 +729,35 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.13" + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false } }, "nbformat": 4, diff --git a/docs/source/tutorials/8_LCA.ipynb b/docs/source/tutorials/8_LCA.ipynb index 0c4819d8..998c86a0 100644 --- a/docs/source/tutorials/8_LCA.ipynb +++ b/docs/source/tutorials/8_LCA.ipynb @@ -8,7 +8,7 @@ "\n", "* **Prepared by:**\n", "\n", - " - [Yalin Li](https://qsdsan.readthedocs.io/en/latest/authors/Yalin_Li.html)\n", + " - [Yalin Li](https://qsdsan.readthedocs.io/en/latest/CONTRIBUTING.html)\n", "\n", "* **Covered topics:**\n", "\n", @@ -19,9 +19,9 @@ "\n", "- **Video demo:**\n", "\n", - " - [Tori Morgan](https://qsdsan.readthedocs.io/en/latest/authors/Tori_Morgan.html)\n", + " - [Tori Morgan](https://qsdsan.readthedocs.io/en/latest/CONTRIBUTING.html)\n", "\n", - "To run tutorials in your browser, go to this [Binder page](https://mybinder.org/v2/gh/QSD-Group/QSDsan/main?filepath=%2Fdocs%2Fsource%2Ftutorials).\n", + "To run tutorials in your browser, go to this [Binder page](https://mybinder.org/v2/gh/QSD-Group/QSDsan-env/main?urlpath=git-pull%3Frepo%3Dhttps%253A%252F%252Fgithub.com%252FQSD-group%252FQSDsan%26urlpath%3Dtree%252FQSDsan%252Fdocs%252Fsource%252Ftutorials%26branch%3Dmain).\n", "\n", "You can also watch a video demo on [YouTube](https://youtu.be/ULmFYO8nTrM) (subscriptions & likes appreciated!)." ] @@ -1148,7 +1148,36 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.9.13" + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false } }, "nbformat": 4, diff --git a/docs/source/tutorials/9_Uncertainty_and_Sensitivity_Analyses.ipynb b/docs/source/tutorials/9_Uncertainty_and_Sensitivity_Analyses.ipynb index ba80082b..97c51a74 100644 --- a/docs/source/tutorials/9_Uncertainty_and_Sensitivity_Analyses.ipynb +++ b/docs/source/tutorials/9_Uncertainty_and_Sensitivity_Analyses.ipynb @@ -9,7 +9,7 @@ "\n", "* **Prepared by:**\n", "\n", - " - [Yalin Li](https://qsdsan.readthedocs.io/en/latest/authors/Yalin_Li.html)\n", + " - [Yalin Li](https://qsdsan.readthedocs.io/en/latest/CONTRIBUTING.html)\n", "\n", "* **Covered topics:**\n", "\n", @@ -18,9 +18,9 @@ " \n", "- **Video demo:**\n", "\n", - " - [Hannah Lohman](https://qsdsan.readthedocs.io/en/latest/authors/Hannah_Lohman.html)\n", + " - [Hannah Lohman](https://qsdsan.readthedocs.io/en/latest/CONTRIBUTING.html)\n", "\n", - "To run tutorials in your browser, go to this [Binder page](https://mybinder.org/v2/gh/QSD-Group/QSDsan/main?filepath=%2Fdocs%2Fsource%2Ftutorials).\n", + "To run tutorials in your browser, go to this [Binder page](https://mybinder.org/v2/gh/QSD-Group/QSDsan-env/main?urlpath=git-pull%3Frepo%3Dhttps%253A%252F%252Fgithub.com%252FQSD-group%252FQSDsan%26urlpath%3Dtree%252FQSDsan%252Fdocs%252Fsource%252Ftutorials%26branch%3Dmain).\n", "\n", "You can also watch a video demo on [YouTube](https://youtu.be/_pIfUEda2jc) (subscriptions & likes appreciated!)." ] @@ -767,7 +767,36 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.9.13" + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false } }, "nbformat": 4, diff --git a/docs/source/tutorials/Tutorial_11.ipynb b/docs/source/tutorials/Tutorial_11.ipynb deleted file mode 100644 index 545c1773..00000000 --- a/docs/source/tutorials/Tutorial_11.ipynb +++ /dev/null @@ -1,401 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "9b7ba848", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "This tutorial was made with qsdsan v1.2.5 and exposan v1.2.5\n" - ] - } - ], - "source": [ - "import qsdsan as qs, exposan\n", - "print(f'This tutorial was made with qsdsan v{qs.__version__} and exposan v{exposan.__version__}')" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "a31c8f69", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "System: bsm1_sys\n", - "ins...\n", - "[0] wastewater\n", - " phase: 'l', T: 293.15 K, P: 101325 Pa\n", - " flow (kmol/hr): S_I 23.1\n", - " S_S 53.4\n", - " X_I 39.4\n", - " X_S 155\n", - " X_BH 21.7\n", - " S_NH 1.34\n", - " S_ND 0.381\n", - " ... 4.26e+04\n", - "outs...\n", - "[0] effluent\n", - " phase: 'l', T: 293.15 K, P: 101325 Pa\n", - " flow: 0\n", - "[1] WAS\n", - " phase: 'l', T: 293.15 K, P: 101325 Pa\n", - " flow: 0\n" - ] - } - ], - "source": [ - "# Let's load the BSM1 system first\n", - "from exposan import bsm1\n", - "bsm1.load()\n", - "sys = bsm1.sys\n", - "sys.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "5fe1776f", - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "A1CSTR:c->A2CSTR:c\n", - "\n", - "\n", - "\n", - " ws1\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "A2CSTR:c->O1CSTR:c\n", - "\n", - "\n", - "\n", - " ws3\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "O1CSTR:c->O2CSTR:c\n", - "\n", - "\n", - "\n", - " ws5\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "O2CSTR:c->O3CSTR:c\n", - "\n", - "\n", - "\n", - " ws7\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "O3CSTR:c->A1CSTR:c\n", - "\n", - "\n", - "\n", - " RWW\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "O3CSTR:c->C1Flat bottom circular clarifier:c\n", - "\n", - "\n", - "\n", - " treated\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "C1Flat bottom circular clarifier:c->A1CSTR:c\n", - "\n", - "\n", - "\n", - " RAS\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "C1Flat bottom circular clarifier:c-> effluent:w\n", - "\n", - "\n", - " effluent\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "C1Flat bottom circular clarifier:c-> WAS:w\n", - "\n", - "\n", - " WAS\n", - "\n", - "\n", - "\n", - "\n", - "\n", - " wastewater:e->A1CSTR:c\n", - "\n", - "\n", - " wastewater\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "A1CSTR\n", - "\n", - "\n", - "A1CSTR\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "A2CSTR\n", - "\n", - "\n", - "A2CSTR\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "O1CSTR\n", - "\n", - "\n", - "O1CSTR\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "O2CSTR\n", - "\n", - "\n", - "O2CSTR\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "O3CSTR\n", - "\n", - "\n", - "O3CSTR\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "C1Flat bottom circular clarifier\n", - "\n", - "\n", - "C1Flat bottom circular clarifier\n", - "\n", - "\n", - "\n", - "\n", - "\n", - " wastewater\n", - "\n", - "\n", - "\n", - "\n", - " effluent\n", - "\n", - "\n", - "\n", - "\n", - " WAS\n", - "\n", - "\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# The BSM1 system is composed of 5 CSTRs in series,\n", - "# followed by a flat-bottom circular clarifier.\n", - "sys.diagram()\n", - "# sys.units" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "98d2662c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# We can verify that by\n", - "sys.isdynamic" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "e2c64ce0", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{: True,\n", - " : True,\n", - " : True,\n", - " : True,\n", - " : True,\n", - " : True}" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# This is because the system contains at least one dynamic SanUnit\n", - "{u: u.isdynamic for u in sys.units}\n", - "\n", - "# If we disable dynamic simulation, then `simulate` would work as usual\n", - "# sys.isdynamic = False\n", - "# sys.simulate()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d8cc6e48", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\joy_c\\anaconda3\\envs\\tut\\lib\\site-packages\\qsdsan\\sanunits\\_suspended_growth_bioreactor.py:44: NumbaPerformanceWarning: \u001b[1m\u001b[1m'@' is faster on contiguous arrays, called on (array(float64, 1d, A), array(float64, 2d, A))\u001b[0m\u001b[0m\n", - " flow_in = Q_ins @ C_ins / V_arr\n", - "C:\\Users\\joy_c\\anaconda3\\envs\\tut\\lib\\site-packages\\numba\\core\\typing\\npydecl.py:913: NumbaPerformanceWarning: \u001b[1m'@' is faster on contiguous arrays, called on (array(float64, 1d, A), array(float64, 2d, A))\u001b[0m\n", - " warnings.warn(NumbaPerformanceWarning(msg))\n" - ] - } - ], - "source": [ - "# Let's try simulating the BSM1 system from day 0 to day 50\n", - "sys.simulate(t_span=(0, 50), method='BDF', state_reset_hook='reset_cache')\n", - "sys.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ba51c0b9", - "metadata": {}, - "outputs": [], - "source": [ - "# This shows the units/streams whose state variables are kept track of\n", - "# during dynamic simulations.\n", - "sys.scope.subjects" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c4c0bdfd", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f1d690bf", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c05808bc", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "37df12a9", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python [conda env:tut]", - "language": "python", - "name": "conda-env-tut-py" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/source/tutorials/_index.rst b/docs/source/tutorials/_index.rst index 6fa4cbe7..c32424f2 100644 --- a/docs/source/tutorials/_index.rst +++ b/docs/source/tutorials/_index.rst @@ -28,6 +28,7 @@ Topical Tutorials 9_Uncertainty_and_Sensitivity_Analyses 10_Process 11_Dynamic_Simulation + 12_Anaerobic_Digestion_Model_No_1 Additional Resources diff --git a/docs/source/tutorials/_bkm.tsv b/docs/source/tutorials/assets/_bkm.tsv similarity index 100% rename from docs/source/tutorials/_bkm.tsv rename to docs/source/tutorials/assets/_bkm.tsv diff --git a/docs/source/tutorials/assets/adm1.jpg b/docs/source/tutorials/assets/adm1.jpg new file mode 100644 index 00000000..f3d7dd98 Binary files /dev/null and b/docs/source/tutorials/assets/adm1.jpg differ diff --git a/legacy_files/binder.yaml b/legacy_files/binder.yaml new file mode 100644 index 00000000..0f5690e7 Binary files /dev/null and b/legacy_files/binder.yaml differ diff --git a/qsdsan/_impact_item.py b/qsdsan/_impact_item.py index 98fc476a..395bfb1c 100644 --- a/qsdsan/_impact_item.py +++ b/qsdsan/_impact_item.py @@ -414,7 +414,7 @@ def load_from_file(cls, path_or_dict, index_col=None): This Excel should have multiple sheets: - The "info" sheet should have three columns: "ID" (e.g., Cement) \ - "functional_unit" (e.g., kg), and "kind" ("ImpactItem" or "StreamImpactItem") + "functional_unit" (e.g., kg), and "kind" ("ImpactItem" or "StreamImpactItem") \ of different impact items. - The remaining sheets should contain characterization factors of \ diff --git a/qsdsan/sanunits/_abstract.py b/qsdsan/sanunits/_abstract.py index 8a99237e..0b29272e 100644 --- a/qsdsan/sanunits/_abstract.py +++ b/qsdsan/sanunits/_abstract.py @@ -58,10 +58,11 @@ class Mixer(SanUnit, BSTMixer): _graphics = BSTMixer._graphics def __init__(self, ID='', ins=None, outs=(), thermo=None, init_with='WasteStream', F_BM_default=None, isdynamic=False, - rigorous=False): + rigorous=False, conserve_phases=False): SanUnit.__init__(self, ID, ins, outs, thermo, init_with, F_BM_default=F_BM_default, isdynamic=isdynamic) self.rigorous = rigorous + self.conserve_phases = conserve_phases @property diff --git a/requirements.txt b/requirements.txt index d58c4abe..37a3c2d7 100644 --- a/requirements.txt +++ b/requirements.txt @@ -13,7 +13,4 @@ sphinx-copybutton sphinx-design furo nbsphinx -pandoc -ipywidgets -jupyterlab-widgets -widgetsnbextension \ No newline at end of file +pandoc \ No newline at end of file diff --git a/setup.py b/setup.py index 62bbe8bd..57034c09 100644 --- a/setup.py +++ b/setup.py @@ -18,7 +18,7 @@ setup( name='qsdsan', packages=['qsdsan'], - version='1.3.0', + version='1.3.1', license='UIUC', author='Quantitative Sustainable Design Group', author_email='quantitative.sustainable.design@gmail.com',