{ "cells": [ { "cell_type": "markdown", "id": "8d891055", "metadata": {}, "source": [ "# Process Modeling 101 \n", "\n", "*Click the badge below to try this tutorial interactively in your browser:*\n", "\n", "[![Launch Binder](../images/custom_binder_logo.svg)](https://mybinder.org/v2/gh/QSD-Group/QSDsan-env/main?urlpath=git-pull%3Frepo%3Dhttps%253A%252F%252Fgithub.com%252FQSD-group%252FQSDsan%26urlpath%3Dlab%252Ftree%252FQSDsan%252Fdocs%252Fsource%252Ftutorials%26branch%3Dmain)\n", "\n", "*You can also run this tutorial in [Google Colab](https://colab.research.google.com). It takes a one-time setup per session: follow the [Colab instructions](https://qsdsan.readthedocs.io/en/latest/tutorials/index.html#run-in-colab).*\n", "\n", "- **Prepared by:**\n", "\n", " - [Ga-Yeong Kim](https://github.com/GaYeongKim)\n", " - [Joy Zhang](https://github.com/joyxyz1994/)\n", "\n", "- **Learning objectives.** After this tutorial, you will be able to:\n", "\n", " - Assemble a multi-unit dynamic flowsheet from `CSTR`, `FlatBottomCircularClarifier`, and internal plus external recycle streams\n", " - Attach a biokinetic model (`ASM2d`) and a per-zone aeration model to dynamic reactors\n", " - Initialize reactor states from a file with `batch_init`, run the simulation, and interpret the resulting state-variable trajectories\n", "\n", "- **Prerequisites:**\n", "\n", " - [10. Process](https://qsdsan.readthedocs.io/en/latest/tutorials/10_Process.html)\n", " - [11. Dynamic Simulation](https://qsdsan.readthedocs.io/en/latest/tutorials/11_Dynamic_Simulation.html)\n", " - [12. Anaerobic Digestion Model No. 1](https://qsdsan.readthedocs.io/en/latest/tutorials/12_Anaerobic_Digestion_Model_No_1.html)\n", "\n", "- **Covered topics:**\n", "\n", " - 1. Introduction\n", " - 2. System setup\n", " - 3. System simulation" ] }, { "cell_type": "markdown", "id": "bcb8dc9fe497", "metadata": {}, "source": [ "\n", "\n", "## Setup\n", "\n", "Import `QSDsan` and confirm the installed version.\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "9a2a96b7", "metadata": { "execution": { "iopub.execute_input": "2026-05-29T13:10:30.181123Z", "iopub.status.busy": "2026-05-29T13:10:30.181123Z", "iopub.status.idle": "2026-05-29T13:10:47.223685Z", "shell.execute_reply": "2026-05-29T13:10:47.223685Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This tutorial was made with qsdsan v1.5.3.\n" ] } ], "source": [ "import qsdsan as qs\n", "print(f'This tutorial was made with qsdsan v{qs.__version__}.')" ] }, { "cell_type": "markdown", "id": "1bbdffaa", "metadata": {}, "source": [ "## 1. Introduction " ] }, { "cell_type": "markdown", "id": "9d21a2f5", "metadata": {}, "source": [ "In Tutorials 10-12, we covered the building blocks of process modeling: how a kinetic model is represented ([Tutorial 10](https://qsdsan.readthedocs.io/en/latest/tutorials/10_Process.html) introduced the `Process` / `CompiledProcesses` classes and the Petersen / Gujer matrix), how a single dynamic unit is set up and integrated through time ([Tutorial 11](https://qsdsan.readthedocs.io/en/latest/tutorials/11_Dynamic_Simulation.html)), and how a full biokinetic model lives inside one reactor ([Tutorial 12](https://qsdsan.readthedocs.io/en/latest/tutorials/12_Anaerobic_Digestion_Model_No_1.html) uses ADM1 inside an `AnaerobicCSTR`).\n", "\n", "This tutorial serves both as a review and a practical application example. It puts those pieces together at the *flowsheet* scale: a complete A2O (anaerobic-anoxic-oxic) biological nutrient removal plant, built from five coupled dynamic CSTRs with per-zone aeration, a layered final clarifier, and internal plus external recycle streams. The biokinetic model is **Activated Sludge Model No. 2d (ASM2d)**, the IWA-validated activated-sludge model that resolves carbon, nitrogen, and phosphorus dynamics simultaneously.\n", "\n", "If you want to revisit the conceptual basics (components, stoichiometry, kinetics, the Petersen / Gujer matrix), [Tutorial 10](https://qsdsan.readthedocs.io/en/latest/tutorials/10_Process.html) and [Tutorial 12](https://qsdsan.readthedocs.io/en/latest/tutorials/12_Anaerobic_Digestion_Model_No_1.html) §1 cover them in depth." ] }, { "cell_type": "markdown", "id": "31978d57", "metadata": {}, "source": [ "![example_wastewater_treatment_process.png](assets/tutorial_13/example_wastewater_treatment_process.png)" ] }, { "cell_type": "markdown", "id": "47af6e27", "metadata": {}, "source": [ "## 2. System setup " ] }, { "cell_type": "markdown", "id": "8469bd17", "metadata": {}, "source": [ "### 2.1. `Component`" ] }, { "cell_type": "markdown", "id": "9b8aa9ec", "metadata": {}, "source": [ "Components were defined conceptually in [Tutorial 2](https://qsdsan.readthedocs.io/en/latest/tutorials/2_Component.html). Here, instead of constructing each one by one, we use the precompiled ASM2d component set shipped with QSDsan." ] }, { "cell_type": "markdown", "id": "2d1eb51e", "metadata": {}, "source": [ "![component.png](assets/tutorial_13/component.png)" ] }, { "cell_type": "code", "execution_count": 2, "id": "336a8a6a", "metadata": { "execution": { "iopub.execute_input": "2026-05-29T13:10:47.226743Z", "iopub.status.busy": "2026-05-29T13:10:47.226743Z", "iopub.status.idle": "2026-05-29T13:10:47.294128Z", "shell.execute_reply": "2026-05-29T13:10:47.294128Z" } }, "outputs": [], "source": [ "# Import packages\n", "import numpy as np, pandas as pd\n", "from qsdsan import unit_operations as su, process_models as pc, WasteStream, System\n", "from qsdsan.utils import time_printer, load_data, get_SRT" ] }, { "cell_type": "code", "execution_count": 3, "id": "03ccebe2", "metadata": { "execution": { "iopub.execute_input": "2026-05-29T13:10:47.294128Z", "iopub.status.busy": "2026-05-29T13:10:47.294128Z", "iopub.status.idle": "2026-05-29T13:10:47.518139Z", "shell.execute_reply": "2026-05-29T13:10:47.518139Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CompiledComponents([\n", " S_O2, S_N2, S_NH4, S_NO3,\n", " S_PO4, S_F, S_A, S_I, \n", " S_ALK, X_I, X_S, X_H, \n", " X_PAO, X_PP, X_PHA, X_AUT,\n", " X_MeOH, X_MeP, H2O, \n", "])\n" ] } ], "source": [ "# Components \n", "cmps = pc.create_asm2d_cmps() # create components of ASM2d \n", " # you don't need to define each component one by one. \n", " # compiled components for ASM2d are already available.\n", " \n", "cmps.show() # 18 components of ASM2d + water (X_TSS was excluded due to redundancy.)" ] }, { "cell_type": "markdown", "id": "c2875514", "metadata": {}, "source": [ "- **S_O2**: Dissolved oxygen\n", "- **S_N2**: Dinitrogen\n", "- **S_NH4**: Ammonium plus ammonia nitrogen\n", "- **S_NO3**: Nitrate plus nitrite nitrogen (NO3-N + NO2-N)\n", "- **S_PO4**: Inorganic soluble phosphorus, primarily orthophosphates\n", "- **S_F**: Fermentable, readily biodegradable organic substrates\n", "- **S_A**: Fermentation products, considered to be acetate\n", "- **S_I**: Inert soluble organic material\n", "- **S_ALK**: Alkalinity of the wastewater\n", "- **X_I**: Inert particulate organic material\n", "- **X_S**: Slowly biodegradable substrates\n", "- **X_H**: Heterotrophic organisms\n", "- **X_PAO**: Phosphate-accumulating organisms (PAO)\n", "- **X_PP**: Poly-phosphate\n", "- **X_PHA**: A cell-internal storage product of PAO\n", "- **X_AUT**: Nitrifying organisms\n", "- **X_MeOH**: Metal-hydroxides\n", "- **X_MeP**: Metal-phosphate (MePO4)" ] }, { "cell_type": "code", "execution_count": 4, "id": "5bfda984", "metadata": { "execution": { "iopub.execute_input": "2026-05-29T13:10:47.521463Z", "iopub.status.busy": "2026-05-29T13:10:47.521463Z", "iopub.status.idle": "2026-05-29T13:10:47.526136Z", "shell.execute_reply": "2026-05-29T13:10:47.525130Z" }, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Component: S_A (phase_ref='l')\n", "[Names] CAS: 71-50-1\n", " InChI: C2H4O2/c1-2(3)4/h1H3...\n", " InChI_key: QTBSBXVTEAMEQO-U...\n", " common_name: acetate ion\n", " iupac_name: ('acetate',)\n", " pubchemid: 175\n", " smiles: CC(=O)[O-]\n", " formula: C2H3O2-\n", "[Groups] Dortmund: \n", " UNIFAC: \n", " PSRK: \n", " NIST: \n", "[Data] MW: 63.998 g/mol\n", " Tm: None\n", " Tb: 626.15 K\n", " Tt: None\n", " Tc: None\n", " Pt: None\n", " Pc: None\n", " Vc: 0.00016858 m^3/mol\n", " Hf: 0 J/mol\n", " S0: 0 J/K/mol\n", " LHV: 0 J/mol\n", " HHV: 0 J/mol\n", " Hfus: 0 J/mol\n", " Sfus: 0\n", " omega: None\n", " dipole: None\n", " similarity_variable: 0.11856\n", " iscyclic_aliphatic: 0\n", " combustion: {'CO2': 2, 'O2'...\n", "Component-specific properties:\n", "[Others] measured_as: COD\n", " description: Acetate\n", " particle_size: Soluble\n", " degradability: Readily\n", " organic: True\n", " i_C: 0.37535 g C/g COD\n", " i_N: 0 g N/g COD\n", " i_P: 0 g P/g COD\n", " i_K: 0 g K/g COD\n", " i_Mg: 0 g Mg/g COD\n", " i_Ca: 0 g Ca/g COD\n", " i_mass: 0.9226 g mass/g COD\n", " i_charge: -0.015626 mol +/g COD\n", " i_COD: 1 g COD/g COD\n", " i_NOD: 0 g NOD/g COD\n", " f_BOD5_COD: 0.717\n", " f_uBOD_COD: 0.8628\n", " f_Vmass_Totmass: 1\n", " chem_MW: 59.044\n" ] } ], "source": [ "cmps.S_A.show(chemical_info=True) # each component stores thermodynamic properties." ] }, { "cell_type": "markdown", "id": "9bcfbee8", "metadata": {}, "source": [ "### 2.2. `WasteStream`" ] }, { "cell_type": "markdown", "id": "bdd68560", "metadata": {}, "source": [ "`WasteStream` was introduced in [Tutorial 3](https://qsdsan.readthedocs.io/en/latest/tutorials/3_WasteStream.html). Here we create the influent, effluent, and recycle streams as empty `WasteStream` objects, then set the influent composition by concentration." ] }, { "cell_type": "markdown", "id": "ae0f430a", "metadata": {}, "source": [ "![waste_stream.png](assets/tutorial_13/waste_stream.png)" ] }, { "cell_type": "code", "execution_count": 5, "id": "24a56109", "metadata": { "execution": { "iopub.execute_input": "2026-05-29T13:10:47.527178Z", "iopub.status.busy": "2026-05-29T13:10:47.527178Z", "iopub.status.idle": "2026-05-29T13:10:47.535303Z", "shell.execute_reply": "2026-05-29T13:10:47.535303Z" } }, "outputs": [], "source": [ "# Parameters (flowrates, temperature)\n", "Q_inf = 18446 # influent flowrate [m3/d]\n", "Q_was = 385 # sludge wastage flowrate [m3/d]\n", "Q_ext = 18446 # external recycle flowrate [m3/d]\n", " # internal recycle flowrate will be defined later using split ratio.\n", " # effluent flowrate will be calculated as the amount remaining after recycling and wastage. \n", "\n", "Temp = 273.15+20 # temperature [K]" ] }, { "cell_type": "code", "execution_count": 6, "id": "3ea89fed", "metadata": { "execution": { "iopub.execute_input": "2026-05-29T13:10:47.538309Z", "iopub.status.busy": "2026-05-29T13:10:47.537308Z", "iopub.status.idle": "2026-05-29T13:10:47.541463Z", "shell.execute_reply": "2026-05-29T13:10:47.541463Z" } }, "outputs": [], "source": [ "# Create influent, effluent, recycle stream\n", "influent = WasteStream('influent', T=Temp) # create an empty wastestream with specified temperature\n", "effluent = WasteStream('effluent', T=Temp)\n", "\n", "int_recycle = WasteStream('internal_recycle', T=Temp)\n", "ext_recycle = WasteStream('external_recycle', T=Temp)\n", "wastage = WasteStream('wastage', T=Temp) # streams between the reactors will be \n", " # automatically assigned when we define SanUnit." ] }, { "cell_type": "code", "execution_count": 7, "id": "2ca166f4", "metadata": { "execution": { "iopub.execute_input": "2026-05-29T13:10:47.542772Z", "iopub.status.busy": "2026-05-29T13:10:47.542772Z", "iopub.status.idle": "2026-05-29T13:10:47.561065Z", "shell.execute_reply": "2026-05-29T13:10:47.561065Z" }, "scrolled": true }, "outputs": [], "source": [ "# Set the influent composition\n", "default_inf_kwargs = { # default influent composition\n", " 'concentrations': { # you can set concentration of each component separately.\n", " 'S_I': 14,\n", " 'X_I': 26.5,\n", " 'S_F': 20.1,\n", " 'S_A': 94.3,\n", " 'X_S': 409.75,\n", " 'S_NH4': 31,\n", " 'S_N2': 0,\n", " 'S_NO3': 0.266, \n", " 'S_PO4': 2.8,\n", " 'X_PP': 0.05,\n", " 'X_PHA': 0.5,\n", " 'X_H': 0.15,\n", " 'X_AUT': 0, \n", " 'X_PAO': 0, \n", " 'S_ALK':7*12,\n", " },\n", " 'units': ('m3/d', 'mg/L'), # ('input total flowrate', 'input concentrations')\n", " } \n", "\n", "influent.set_flow_by_concentration(Q_inf, **default_inf_kwargs) # set flowrate and composition of empty influent WasteStream" ] }, { "cell_type": "code", "execution_count": 8, "id": "8bbfc5bd", "metadata": { "execution": { "iopub.execute_input": "2026-05-29T13:10:47.561065Z", "iopub.status.busy": "2026-05-29T13:10:47.561065Z", "iopub.status.idle": "2026-05-29T13:10:47.693053Z", "shell.execute_reply": "2026-05-29T13:10:47.692745Z" }, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WasteStream: influent\n", "phase: 'l', T: 293.15 K, P: 101325 Pa\n", "flow (g/hr): S_NH4 2.38e+04\n", " S_NO3 204\n", " S_PO4 2.15e+03\n", " S_F 1.54e+04\n", " S_A 7.25e+04\n", " S_I 1.08e+04\n", " S_ALK 6.46e+04\n", " X_I 2.04e+04\n", " X_S 3.15e+05\n", " X_H 115\n", " X_PP 38.4\n", " X_PHA 384\n", " H2O 7.66e+08\n", " WasteStream-specific properties:\n", " pH : 7.0\n", " Alkalinity : 2.5 mmol/L\n", " COD : 565.3 mg/L\n", " BOD : 320.1 mg/L\n", " TC : 271.4 mg/L\n", " TOC : 187.4 mg/L\n", " TN : 48.9 mg/L\n", " TP : 7.4 mg/L\n", " TK : 0.1 mg/L\n", " TSS : 327.8 mg/L\n", " Component concentrations (mg/L):\n", " S_NH4 31.0\n", " S_NO3 0.3\n", " S_PO4 2.8\n", " S_F 20.1\n", " S_A 94.3\n", " S_I 14.0\n", " S_ALK 84.0\n", " X_I 26.5\n", " X_S 409.8\n", " X_H 0.2\n", " X_PP 0.1\n", " X_PHA 0.5\n", " H2O 997254.9\n" ] } ], "source": [ "# Wastestream stores bulk properties of the stream, as well as concentration of each component.\n", "influent.show()" ] }, { "cell_type": "code", "execution_count": 9, "id": "b7eb93e0", "metadata": { "execution": { "iopub.execute_input": "2026-05-29T13:10:47.695083Z", "iopub.status.busy": "2026-05-29T13:10:47.695083Z", "iopub.status.idle": "2026-05-29T13:10:47.709426Z", "shell.execute_reply": "2026-05-29T13:10:47.708414Z" } }, "outputs": [ { "data": { "text/plain": [ "324.98437505925017" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "influent.get_VSS() # you can also retrieve other information, such as VSS, TSS, TDS, etc." ] }, { "cell_type": "markdown", "id": "840641b7", "metadata": {}, "source": [ "### 2.3. `Process`" ] }, { "cell_type": "markdown", "id": "6b1782f5", "metadata": {}, "source": [ "`Process` was covered in depth in [Tutorial 10](https://qsdsan.readthedocs.io/en/latest/tutorials/10_Process.html). In this tutorial we use the precompiled ASM2d biokinetic processes together with a `DiffusedAeration` process attached to each aerated zone to model oxygen mass transfer." ] }, { "cell_type": "markdown", "id": "220da432", "metadata": {}, "source": [ "#### 2.3.1. Aeration " ] }, { "cell_type": "markdown", "id": "93809a68", "metadata": {}, "source": [ "![aeration_process.png](assets/tutorial_13/aeration_process.png)" ] }, { "cell_type": "code", "execution_count": 10, "id": "d674d59c", "metadata": { "execution": { "iopub.execute_input": "2026-05-29T13:10:47.710482Z", "iopub.status.busy": "2026-05-29T13:10:47.710482Z", "iopub.status.idle": "2026-05-29T13:10:47.714763Z", "shell.execute_reply": "2026-05-29T13:10:47.714763Z" } }, "outputs": [], "source": [ "# Parameters (volumes)\n", "V_an = 1000 # anoxic zone tank volume [m3/d]\n", "V_ae = 1333 # aerated zone tank volume [m3/d]" ] }, { "cell_type": "code", "execution_count": 11, "id": "8b53ebff", "metadata": { "execution": { "iopub.execute_input": "2026-05-29T13:10:47.714763Z", "iopub.status.busy": "2026-05-29T13:10:47.714763Z", "iopub.status.idle": "2026-05-29T13:10:48.166666Z", "shell.execute_reply": "2026-05-29T13:10:48.166666Z" } }, "outputs": [], "source": [ "# Aeration model\n", "aer1 = pc.DiffusedAeration('aer1', DO_ID='S_O2', KLa=240, DOsat=8.0, V=V_ae) # aeration model for Tank 3 & Tank 4\n", "aer2 = pc.DiffusedAeration('aer2', DO_ID='S_O2', KLa=84, DOsat=8.0, V=V_ae) # aeration model for Tank 5" ] }, { "cell_type": "markdown", "id": "4a6520b6", "metadata": {}, "source": [ "- **DO_ID:** The component ID of dissolved oxygen (DO).
\n", "- **KLa:** Oxygen mass transfer coefficient.
\n", "- **DOsat:** Surface DO saturation concentration.
\n", "- **V:** Reactor volume" ] }, { "cell_type": "code", "execution_count": 12, "id": "220e8d6a", "metadata": { "execution": { "iopub.execute_input": "2026-05-29T13:10:48.166666Z", "iopub.status.busy": "2026-05-29T13:10:48.166666Z", "iopub.status.idle": "2026-05-29T13:10:48.174848Z", "shell.execute_reply": "2026-05-29T13:10:48.174848Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Process: aer1\n", "[stoichiometry] S_O2: 1\n", "[reference] S_O2\n", "[rate equation] KLa*(DOsat - S_O2)\n", "[parameters] KLa: 240\n", " DOsat: 8\n", "[dynamic parameters] \n" ] } ], "source": [ "aer1.show()" ] }, { "cell_type": "markdown", "id": "69bdece7", "metadata": {}, "source": [ "#### 2.3.2. ASM2d" ] }, { "cell_type": "markdown", "id": "61fc2839", "metadata": {}, "source": [ "![asm2d_process.png](assets/tutorial_13/asm2d_process.png)" ] }, { "cell_type": "code", "execution_count": 13, "id": "a988b174", "metadata": { "execution": { "iopub.execute_input": "2026-05-29T13:10:48.174848Z", "iopub.status.busy": "2026-05-29T13:10:48.174848Z", "iopub.status.idle": "2026-05-29T13:10:51.146533Z", "shell.execute_reply": "2026-05-29T13:10:51.144967Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ASM2d([aero_hydrolysis, anox_hydrolysis, anae_hydrolysis, hetero_growth_S_F, hetero_growth_S_A, denitri_S_F, denitri_S_A, ferment, hetero_lysis, PAO_storage_PHA, aero_storage_PP, anox_storage_PP, PAO_aero_growth_PHA, PAO_anox_growth, PAO_lysis, PP_lysis, PHA_lysis, auto_aero_growth, auto_lysis, precipitation, redissolution])\n" ] } ], "source": [ "# ASM2d \n", "asm2d = pc.ASM2d() # create ASM2d processes\n", "asm2d.show() # 21 processes in ASM2d" ] }, { "cell_type": "code", "execution_count": 14, "id": "073b8dbd", "metadata": { "execution": { "iopub.execute_input": "2026-05-29T13:10:51.149626Z", "iopub.status.busy": "2026-05-29T13:10:51.149626Z", "iopub.status.idle": "2026-05-29T13:10:51.155858Z", "shell.execute_reply": "2026-05-29T13:10:51.155858Z" }, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Process: aero_hydrolysis\n", "[stoichiometry] S_NH4: 0.02*f_SI + 0.01\n", " S_PO4: 0.01*f_SI\n", " S_F: 1.0 - 1.0*f_SI\n", " S_I: 1.0*f_SI\n", " S_ALK: 0.0113*f_SI + 0.00858\n", " X_S: -1.00\n", "[reference] X_S\n", "[rate equation] K_h*S_O2*X_S/((K_O2 + S_O2)*...\n", "[parameters] f_SI: 0\n", " Y_H: 0.625\n", " f_XI_H: 0.1\n", " Y_PAO: 0.625\n", " Y_PO4: 0.4\n", " Y_PHA: 0.2\n", " f_XI_PAO: 0.1\n", " Y_A: 0.24\n", " f_XI_AUT: 0.1\n", " K_h: 3\n", " eta_NO3: 0.6\n", " eta_fe: 0.4\n", " K_O2: 0.2\n", " K_NO3: 0.5\n", " K_X: 0.1\n", " mu_H: 6\n", " q_fe: 3\n", " eta_NO3_H: 0.8\n", " b_H: 0.4\n", " K_O2_H: 0.2\n", " K_F: 4\n", " K_fe: 4\n", " K_A_H: 4\n", " K_NO3_H: 0.5\n", " K_NH4_H: 0.05\n", " K_P_H: 0.01\n", " K_ALK_H: 1.2\n", " q_PHA: 3\n", " q_PP: 1.5\n", " mu_PAO: 1\n", " eta_NO3_PAO: 0.6\n", " b_PAO: 0.2\n", " b_PP: 0.2\n", " b_PHA: 0.2\n", " K_O2_PAO: 0.2\n", " K_NO3_PAO: 0.5\n", " K_A_PAO: 4\n", " K_NH4_PAO: 0.05\n", " K_PS: 0.2\n", " K_P_PAO: 0.01\n", " K_ALK_PAO: 1.2\n", " K_PP: 0.01\n", " K_MAX: 0.34\n", " K_IPP: 0.02\n", " K_PHA: 0.01\n", " mu_AUT: 1\n", " b_AUT: 0.15\n", " K_O2_AUT: 0.5\n", " K_NH4_AUT: 1\n", " K_ALK_AUT: 6\n", " K_P_AUT: 0.01\n", " k_PRE: 1\n", " k_RED: 0.6\n", " K_ALK_PRE: 6\n", " COD_deN: 2.86\n", "[dynamic parameters] \n" ] } ], "source": [ "asm2d.aero_hydrolysis # Each process includes stoichiometry, rate equation, and parameters." ] }, { "cell_type": "code", "execution_count": 15, "id": "82cf1c38", "metadata": { "execution": { "iopub.execute_input": "2026-05-29T13:10:51.157870Z", "iopub.status.busy": "2026-05-29T13:10:51.157870Z", "iopub.status.idle": "2026-05-29T13:10:51.647539Z", "shell.execute_reply": "2026-05-29T13:10:51.647539Z" }, "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
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S_O2S_N2S_NH4S_NO3S_PO4S_FS_AS_IS_ALKX_IX_SX_HX_PAOX_PPX_PHAX_AUTX_MeOHX_MePH2O
aero_hydrolysis000.01001000.008570-100000000
anox_hydrolysis000.01001000.008570-100000000
anae_hydrolysis000.01001000.008570-100000000
hetero_growth_S_F-0.60-0.0220-0.004-1.600-0.01650010000000
hetero_growth_S_A-0.60-0.070-0.020-1.600.2520010000000
denitri_S_F00.21-0.022-0.21-0.004-1.6000.1640010000000
denitri_S_A00.21-0.07-0.21-0.020-1.600.4320010000000
ferment000.0300.01-110-0.1680000000000
hetero_lysis000.03200.010000.02160.10.9-10000000
PAO_storage_PHA00000.40-100.110000-0.410000
aero_storage_PP-0.2000-10000.19400001-0.20000
anox_storage_PP00.07-7.78e-18-0.07-10000.25400001-0.20000
PAO_aero_growth_PHA-0.60-0.070-0.02000-0.048400010-1.60000
PAO_anox_growth00.21-0.07-0.21-0.020000.13200010-1.60000
PAO_lysis000.03200.010000.02160.10.90-1000000
PP_lysis00001000-0.1940000-100000
PHA_lysis00000010-0.18800000-10000
auto_aero_growth-180-4.244.17-0.02000-7.190000001000
auto_lysis000.03200.010000.02160.10.90000-1000
precipitation0000-10000.5820000000-3.454.870
redissolution00001000-0.58200000003.45-4.870
\n", "
" ], "text/plain": [ " S_O2 S_N2 S_NH4 S_NO3 S_PO4 S_F S_A S_I S_ALK X_I X_S X_H X_PAO X_PP X_PHA X_AUT X_MeOH X_MeP H2O\n", "aero_hydrolysis 0 0 0.01 0 0 1 0 0 0.00857 0 -1 0 0 0 0 0 0 0 0\n", "anox_hydrolysis 0 0 0.01 0 0 1 0 0 0.00857 0 -1 0 0 0 0 0 0 0 0\n", "anae_hydrolysis 0 0 0.01 0 0 1 0 0 0.00857 0 -1 0 0 0 0 0 0 0 0\n", "hetero_growth_S_F -0.6 0 -0.022 0 -0.004 -1.6 0 0 -0.0165 0 0 1 0 0 0 0 0 0 0\n", "hetero_growth_S_A -0.6 0 -0.07 0 -0.02 0 -1.6 0 0.252 0 0 1 0 0 0 0 0 0 0\n", "denitri_S_F 0 0.21 -0.022 -0.21 -0.004 -1.6 0 0 0.164 0 0 1 0 0 0 0 0 0 0\n", "denitri_S_A 0 0.21 -0.07 -0.21 -0.02 0 -1.6 0 0.432 0 0 1 0 0 0 0 0 0 0\n", "ferment 0 0 0.03 0 0.01 -1 1 0 -0.168 0 0 0 0 0 0 0 0 0 0\n", "hetero_lysis 0 0 0.032 0 0.01 0 0 0 0.0216 0.1 0.9 -1 0 0 0 0 0 0 0\n", "PAO_storage_PHA 0 0 0 0 0.4 0 -1 0 0.11 0 0 0 0 -0.4 1 0 0 0 0\n", "aero_storage_PP -0.2 0 0 0 -1 0 0 0 0.194 0 0 0 0 1 -0.2 0 0 0 0\n", "anox_storage_PP 0 0.07 -7.78e-18 -0.07 -1 0 0 0 0.254 0 0 0 0 1 -0.2 0 0 0 0\n", "PAO_aero_growth_PHA -0.6 0 -0.07 0 -0.02 0 0 0 -0.0484 0 0 0 1 0 -1.6 0 0 0 0\n", "PAO_anox_growth 0 0.21 -0.07 -0.21 -0.02 0 0 0 0.132 0 0 0 1 0 -1.6 0 0 0 0\n", "PAO_lysis 0 0 0.032 0 0.01 0 0 0 0.0216 0.1 0.9 0 -1 0 0 0 0 0 0\n", "PP_lysis 0 0 0 0 1 0 0 0 -0.194 0 0 0 0 -1 0 0 0 0 0\n", "PHA_lysis 0 0 0 0 0 0 1 0 -0.188 0 0 0 0 0 -1 0 0 0 0\n", "auto_aero_growth -18 0 -4.24 4.17 -0.02 0 0 0 -7.19 0 0 0 0 0 0 1 0 0 0\n", "auto_lysis 0 0 0.032 0 0.01 0 0 0 0.0216 0.1 0.9 0 0 0 0 -1 0 0 0\n", "precipitation 0 0 0 0 -1 0 0 0 0.582 0 0 0 0 0 0 0 -3.45 4.87 0\n", "redissolution 0 0 0 0 1 0 0 0 -0.582 0 0 0 0 0 0 0 3.45 -4.87 0" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Petersen stoichiometric matrix of ASM2d\n", "pd.set_option('display.max_columns', None) # to display all columns\n", "\n", "asm2d.stoichiometry" ] }, { "cell_type": "code", "execution_count": 16, "id": "db2be131", "metadata": { "execution": { "iopub.execute_input": "2026-05-29T13:10:51.649288Z", "iopub.status.busy": "2026-05-29T13:10:51.649288Z", "iopub.status.idle": "2026-05-29T13:10:52.095619Z", "shell.execute_reply": "2026-05-29T13:10:52.094579Z" }, "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
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rate_equation
aero_hydrolysis3.0*S_O2*X_S/((0.1 + X_S/X_H)*(...
anox_hydrolysis0.36*S_NO3*X_S/((0.1 + X_S/X_H)...
anae_hydrolysis0.12*X_S/((0.1 + X_S/X_H)*(S_NO...
hetero_growth_S_F6.0*S_ALK*S_F**2*S_NH4*S_O2*S_P...
hetero_growth_S_A6.0*S_A**2*S_ALK*S_NH4*S_O2*S_P...
denitri_S_F0.96*S_ALK*S_F**2*S_NH4*S_NO3*S...
denitri_S_A0.96*S_A**2*S_ALK*S_NH4*S_NO3*S...
ferment0.3*S_ALK*S_F*X_H/((S_ALK + 1.2...
hetero_lysis0.4*X_H
PAO_storage_PHA3.0*S_A*S_ALK*X_PP/((0.01 + X_P...
aero_storage_PP1.5*S_ALK*S_O2*S_PO4*X_PHA*(0.3...
anox_storage_PP0.18*S_ALK*S_NO3*S_PO4*X_PHA*(0...
PAO_aero_growth_PHA1.0*S_ALK*S_NH4*S_O2*S_PO4*X_PH...
PAO_anox_growth0.12*S_ALK*S_NH4*S_NO3*S_PO4*X_...
PAO_lysis0.2*S_ALK*X_PAO/(S_ALK + 1.2)
PP_lysis0.2*S_ALK*X_PP/(S_ALK + 1.2)
PHA_lysis0.2*S_ALK*X_PHA/(S_ALK + 1.2)
auto_aero_growth1.0*S_ALK*S_NH4*S_O2*S_PO4*X_AU...
auto_lysis0.15*X_AUT
precipitation1.0*S_PO4*X_MeOH
redissolution0.6*S_ALK*X_MeP/(S_ALK + 6.0)
\n", "
" ], "text/plain": [ " rate_equation\n", "aero_hydrolysis 3.0*S_O2*X_S/((0.1 + X_S/X_H)*(...\n", "anox_hydrolysis 0.36*S_NO3*X_S/((0.1 + X_S/X_H)...\n", "anae_hydrolysis 0.12*X_S/((0.1 + X_S/X_H)*(S_NO...\n", "hetero_growth_S_F 6.0*S_ALK*S_F**2*S_NH4*S_O2*S_P...\n", "hetero_growth_S_A 6.0*S_A**2*S_ALK*S_NH4*S_O2*S_P...\n", "denitri_S_F 0.96*S_ALK*S_F**2*S_NH4*S_NO3*S...\n", "denitri_S_A 0.96*S_A**2*S_ALK*S_NH4*S_NO3*S...\n", "ferment 0.3*S_ALK*S_F*X_H/((S_ALK + 1.2...\n", "hetero_lysis 0.4*X_H\n", "PAO_storage_PHA 3.0*S_A*S_ALK*X_PP/((0.01 + X_P...\n", "aero_storage_PP 1.5*S_ALK*S_O2*S_PO4*X_PHA*(0.3...\n", "anox_storage_PP 0.18*S_ALK*S_NO3*S_PO4*X_PHA*(0...\n", "PAO_aero_growth_PHA 1.0*S_ALK*S_NH4*S_O2*S_PO4*X_PH...\n", "PAO_anox_growth 0.12*S_ALK*S_NH4*S_NO3*S_PO4*X_...\n", "PAO_lysis 0.2*S_ALK*X_PAO/(S_ALK + 1.2)\n", "PP_lysis 0.2*S_ALK*X_PP/(S_ALK + 1.2)\n", "PHA_lysis 0.2*S_ALK*X_PHA/(S_ALK + 1.2)\n", "auto_aero_growth 1.0*S_ALK*S_NH4*S_O2*S_PO4*X_AU...\n", "auto_lysis 0.15*X_AUT\n", "precipitation 1.0*S_PO4*X_MeOH\n", "redissolution 0.6*S_ALK*X_MeP/(S_ALK + 6.0)" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Rate equations of ASM2d\n", "asm2d.rate_equations" ] }, { "cell_type": "markdown", "id": "d3fba830", "metadata": {}, "source": [ "### 2.4. `SanUnit`" ] }, { "cell_type": "markdown", "id": "4a272aee", "metadata": {}, "source": [ "Static `SanUnit`s were introduced in [Tutorial 4](https://qsdsan.readthedocs.io/en/latest/tutorials/4_SanUnit_basic.html) and [Tutorial 5](https://qsdsan.readthedocs.io/en/latest/tutorials/5_SanUnit_advanced.html); a single dynamic unit was the focus of [Tutorial 11](https://qsdsan.readthedocs.io/en/latest/tutorials/11_Dynamic_Simulation.html). Here we assemble five dynamic `CSTR`s, each carrying both an aeration and a suspended-growth biokinetic model, plus a 10-layer `FlatBottomCircularClarifier` for final settling." ] }, { "cell_type": "markdown", "id": "cd545489", "metadata": {}, "source": [ "![san_unit.png](assets/tutorial_13/san_unit.png)" ] }, { "cell_type": "code", "execution_count": 17, "id": "56dada9c", "metadata": { "execution": { "iopub.execute_input": "2026-05-29T13:10:52.099835Z", "iopub.status.busy": "2026-05-29T13:10:52.099835Z", "iopub.status.idle": "2026-05-29T13:10:52.104906Z", "shell.execute_reply": "2026-05-29T13:10:52.104386Z" } }, "outputs": [], "source": [ "# Anoxic reactors (Tank 1 & Tank 2)\n", "A1 = su.CSTR('A1', ins=[influent, int_recycle, ext_recycle], V_max=V_an, # As CSTR, 3 input streams, tank volume as V_an\n", " aeration=None, suspended_growth_model=asm2d) # No aeration, biokinetic model as asm2d\n", " \n", "A2 = su.CSTR('A2', ins=A1-0, V_max=V_an, # ins=A1-0: set influent of A2 as effluent of A1 reactor (to connect A1 with A2)\n", " aeration=None, suspended_growth_model=asm2d)" ] }, { "cell_type": "markdown", "id": "de27e104", "metadata": {}, "source": [ "- **ins:** influents to the reactor.
\n", "- **outs:** treated effluent from the reactor.
\n", "- **V_max:** designed reactor volume [m^3].
\n", "- **aeration:** aeration setting: a target dissolved oxygen concentration [mg O2/L], a `Process` object representing - aeration, or `None` for no aeration.
\n", "- **suspended_growth_model:** the suspended-growth biokinetic model.\n", "\n", "See the [CSTR documentation](https://qsdsan.readthedocs.io/en/latest/api/unit_operations/dynamic/suspended_growth_bioreactor.html#qsdsan.unit_operations.dynamic._bioreactor.CSTR) for the full signature and default values (you can also run `?su.CSTR` in IPython)." ] }, { "cell_type": "code", "execution_count": 18, "id": "7ca7a5c0", "metadata": { "execution": { "iopub.execute_input": "2026-05-29T13:10:52.107578Z", "iopub.status.busy": "2026-05-29T13:10:52.106560Z", "iopub.status.idle": "2026-05-29T13:10:52.818446Z", "shell.execute_reply": "2026-05-29T13:10:52.818446Z" }, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CSTR: A1\n", "ins...\n", "[0] influent\n", "phase: 'l', T: 293.15 K, P: 101325 Pa\n", "flow (g/hr): S_NH4 2.38e+04\n", " S_NO3 204\n", " S_PO4 2.15e+03\n", " S_F 1.54e+04\n", " S_A 7.25e+04\n", " S_I 1.08e+04\n", " S_ALK 6.46e+04\n", " X_I 2.04e+04\n", " X_S 3.15e+05\n", " X_H 115\n", " X_PP 38.4\n", " X_PHA 384\n", " H2O 7.66e+08\n", " WasteStream-specific properties:\n", " pH : 7.0\n", " Alkalinity : 2.5 mmol/L\n", " COD : 565.3 mg/L\n", " BOD : 320.1 mg/L\n", " TC : 271.4 mg/L\n", " TOC : 187.4 mg/L\n", " TN : 48.9 mg/L\n", " TP : 7.4 mg/L\n", " TK : 0.1 mg/L\n", " TSS : 327.8 mg/L\n", "[1] internal_recycle\n", "phase: 'l', T: 293.15 K, P: 101325 Pa\n", "flow: 0\n", " WasteStream-specific properties: None for empty waste streams\n", "[2] external_recycle\n", "phase: 'l', T: 293.15 K, P: 101325 Pa\n", "flow: 0\n", " WasteStream-specific properties: None for empty waste streams\n", "outs...\n", "[0] ws1 to CSTR-A2\n", "phase: 'l', T: 298.15 K, P: 101325 Pa\n", "flow: 0\n", " WasteStream-specific properties: None for empty waste streams\n" ] } ], "source": [ "# Before simulation, outs are empty.\n", "A1.show()" ] }, { "cell_type": "code", "execution_count": 19, "id": "d061e18d", "metadata": { "execution": { "iopub.execute_input": "2026-05-29T13:10:52.818446Z", "iopub.status.busy": "2026-05-29T13:10:52.818446Z", "iopub.status.idle": "2026-05-29T13:10:52.825095Z", "shell.execute_reply": "2026-05-29T13:10:52.825095Z" } }, "outputs": [], "source": [ "# Aerated reactors (Tank 3, Tank 4, Tank 5)\n", "O1 = su.CSTR('O1', ins=A2-0, V_max=V_ae, aeration=aer1, # tank volume as V_ae with aeration model1\n", " DO_ID='S_O2', suspended_growth_model=asm2d)\n", " \n", "O2 = su.CSTR('O2', ins=O1-0, V_max=V_ae, aeration=aer1,\n", " DO_ID='S_O2', suspended_growth_model=asm2d)\n", " \n", "O3 = su.CSTR('O3', ins=O2-0, outs=[int_recycle, 'treated'], split=[0.6, 0.4], # 60% of effluent to internal recycle, 40% to clarifier\n", " V_max=V_ae, aeration=aer2,\n", " DO_ID='S_O2', suspended_growth_model=asm2d)" ] }, { "cell_type": "code", "execution_count": 20, "id": "b8beae7b", "metadata": { "execution": { "iopub.execute_input": "2026-05-29T13:10:52.828421Z", "iopub.status.busy": "2026-05-29T13:10:52.828421Z", "iopub.status.idle": "2026-05-29T13:10:53.623874Z", "shell.execute_reply": "2026-05-29T13:10:53.623874Z" }, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CSTR: O3\n", "ins...\n", "[0] ws7 from CSTR-O2\n", "phase: 'l', T: 298.15 K, P: 101325 Pa\n", "flow: 0\n", " WasteStream-specific properties: None for empty waste streams\n", "outs...\n", "[0] internal_recycle to CSTR-A1\n", "phase: 'l', T: 293.15 K, P: 101325 Pa\n", "flow: 0\n", " WasteStream-specific properties: None for empty waste streams\n", "[1] treated\n", "phase: 'l', T: 298.15 K, P: 101325 Pa\n", "flow: 0\n", " WasteStream-specific properties: None for empty waste streams\n" ] } ], "source": [ "O3.show()" ] }, { "cell_type": "code", "execution_count": 21, "id": "3d84771e", "metadata": { "execution": { "iopub.execute_input": "2026-05-29T13:10:53.623874Z", "iopub.status.busy": "2026-05-29T13:10:53.623874Z", "iopub.status.idle": "2026-05-29T13:10:53.630374Z", "shell.execute_reply": "2026-05-29T13:10:53.630374Z" } }, "outputs": [], "source": [ "# Clarifier\n", "C1 = su.FlatBottomCircularClarifier('C1', ins=O3-1, outs=[effluent, ext_recycle, wastage], # O3-1: second effluent of O3, three outs\n", " underflow=Q_ext, wastage=Q_was, surface_area=1500,\n", " height=4, N_layer=10, feed_layer=5, # modeled as a 10 layer non-reactive unit\n", " X_threshold=3000, v_max=474, v_max_practical=250,\n", " rh=5.76e-4, rp=2.86e-3, fns=2.28e-3)" ] }, { "cell_type": "markdown", "id": "95a2a1ba", "metadata": {}, "source": [ "- **underflow:** designed recycling sludge flowrate (RAS) [m^3/d].
\n", "- **wastage:** designed wasted sludge flowrate (WAS) [m^3/d].
\n", "- **surface_area:** surface area of the clarifier [m^2].
\n", "- **height:** height of the clarifier [m].
\n", "- **N_layer:** number of layers used to model settling.
\n", "- **feed_layer:** the feed layer, counted from the top.
\n", "- **X_threshold:** threshold suspended-solids concentration [g/m^3].
\n", "- **v_max:** maximum theoretical (Vesilind) settling velocity [m/d].
\n", "- **v_max_practical:** maximum practical settling velocity [m/d].
\n", "- **rh:** hindered-zone settling parameter in the double-exponential settling-velocity function [m^3/g].
\n", "- **rp:** flocculant-zone settling parameter in the double-exponential settling-velocity function [m^3/g].
\n", "- **fns:** non-settleable fraction of the suspended solids (dimensionless, within [0, 1]).\n", "\n", "See the [FlatBottomCircularClarifier documentation](https://qsdsan.readthedocs.io/en/latest/api/unit_operations/static/clarifier.html#qsdsan.unit_operations.static._clarifier.FlatBottomCircularClarifier) for the full signature and default values (you can also run `?su.FlatBottomCircularClarifier` in IPython)." ] }, { "cell_type": "markdown", "id": "734238b9", "metadata": {}, "source": [ "### 2.5. `System`" ] }, { "cell_type": "markdown", "id": "2bbf8fe9", "metadata": {}, "source": [ "Static `System` assembly was introduced in [Tutorial 6](https://qsdsan.readthedocs.io/en/latest/tutorials/6_System.html); single-unit dynamic simulation in [Tutorial 11](https://qsdsan.readthedocs.io/en/latest/tutorials/11_Dynamic_Simulation.html). Here we assemble a multi-unit flowsheet with internal plus external recycle streams, set per-reactor initial conditions through `batch_init`, and let the `System` organize unit operations for mass-balance convergence, techno-economic analysis (TEA), and life cycle assessment (LCA)." ] }, { "cell_type": "markdown", "id": "efdf6a6b", "metadata": {}, "source": [ "![system.png](assets/tutorial_13/system.png)" ] }, { "cell_type": "markdown", "id": "b3dafa54", "metadata": {}, "source": [ "#### 2.5.1. Create system" ] }, { "cell_type": "code", "execution_count": 22, "id": "c358d57a", "metadata": { "execution": { "iopub.execute_input": "2026-05-29T13:10:53.630374Z", "iopub.status.busy": "2026-05-29T13:10:53.630374Z", "iopub.status.idle": "2026-05-29T13:10:53.636471Z", "shell.execute_reply": "2026-05-29T13:10:53.636471Z" } }, "outputs": [], "source": [ "# Create system\n", "sys = System('example_system', path=(A1, A2, O1, O2, O3, C1), recycle=(int_recycle, ext_recycle)) # path: the order of reactor" ] }, { "cell_type": "code", "execution_count": 23, "id": "9af34914", "metadata": { "execution": { "iopub.execute_input": "2026-05-29T13:10:53.636471Z", "iopub.status.busy": "2026-05-29T13:10:53.636471Z", "iopub.status.idle": "2026-05-29T13:10:54.218176Z", "shell.execute_reply": "2026-05-29T13:10:54.217167Z" }, "scrolled": false }, "outputs": [ { "data": { "image/svg+xml": [ "\n", "\n", "\n", "\n", "\n", "121470724161:c->121470724755:c\n", "\n", "\n", "\n", " ws1\n", "\n", "\n", "\n", "\n", "\n", "121470724755:c->121470721221:c\n", "\n", "\n", "\n", " ws3\n", "\n", "\n", "\n", "\n", "\n", "121470721221:c->121470728521:c\n", "\n", "\n", "\n", " ws5\n", "\n", "\n", "\n", "\n", "\n", "121470728521:c->121470729010:c\n", "\n", "\n", "\n", " ws7\n", "\n", "\n", "\n", "\n", "\n", "121470729010:c->121470724161:c\n", "\n", "\n", "\n", " internal recycle\n", "\n", "\n", "\n", "\n", "\n", "121470729010:c->121470721362:c\n", "\n", "\n", "\n", " treated\n", "\n", "\n", "\n", "\n", "\n", "121470721362:c->121470724161:c\n", "\n", "\n", "\n", " external recycle\n", "\n", "\n", "\n", "\n", "\n", "121470721362:c->121470168921:w\n", "\n", "\n", " effluent\n", "\n", "\n", "\n", "\n", "\n", "121470721362:c->121470169201:w\n", "\n", "\n", " wastage\n", "\n", "\n", "\n", "\n", "\n", "121470169041:e->121470724161:c\n", "\n", "\n", " influent\n", "\n", "\n", "\n", "\n", "\n", "121470724161\n", "\n", "\n", "A1\n", "CSTR\n", "\n", "\n", "\n", "\n", "\n", "121470724755\n", "\n", "\n", "A2\n", "CSTR\n", "\n", "\n", "\n", "\n", "\n", "121470721221\n", "\n", "\n", "O1\n", "CSTR\n", "\n", "\n", "\n", "\n", "\n", "121470728521\n", "\n", "\n", "O2\n", "CSTR\n", "\n", "\n", "\n", "\n", "\n", "121470729010\n", "\n", "\n", "O3\n", "CSTR\n", "\n", "\n", "\n", "\n", "\n", "121470721362\n", "\n", "\n", "C1\n", "Flat bottom circular clarifier\n", "\n", "\n", "\n", "\n", "\n", "121470169041\n", "\n", "\n", "\n", "\n", "121470168921\n", "\n", "\n", "\n", "\n", "121470169201\n", "\n", "\n", "\n", "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# System diagram\n", "sys.diagram()" ] }, { "cell_type": "markdown", "id": "cf13ca18", "metadata": {}, "source": [ "#### 2.5.2. Set initial conditions of reactors" ] }, { "cell_type": "code", "execution_count": 24, "id": "3ae11625", "metadata": { "execution": { "iopub.execute_input": "2026-05-29T13:10:54.760286Z", "iopub.status.busy": "2026-05-29T13:10:54.760286Z", "iopub.status.idle": "2026-05-29T13:10:54.774262Z", "shell.execute_reply": "2026-05-29T13:10:54.774262Z" } }, "outputs": [], "source": [ "# Import initial condition excel file\n", "df = load_data('assets/tutorial_13/initial_conditions_asm2d.xlsx', sheet='default')" ] }, { "cell_type": "code", "execution_count": 25, "id": "027fff99", "metadata": { "execution": { "iopub.execute_input": "2026-05-29T13:10:54.774262Z", "iopub.status.busy": "2026-05-29T13:10:54.774262Z", "iopub.status.idle": "2026-05-29T13:10:54.784749Z", "shell.execute_reply": "2026-05-29T13:10:54.784749Z" } }, "outputs": [ { "data": { "text/html": [ "
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S_O2S_NH4S_NO3S_PO4S_FS_AS_IS_ALKX_IX_SX_HX_PAOX_PPX_PHAX_AUT
A10.002137.2310.24.450.2110.026515.9672.28e+0384.43.78e+0332237.20.0517218
A20.00122.42.44.246.6853.814.579084.120718.24.253.5911.9
O2216.54.315.481.92.7313.782.661177.31.04e+0386.46.451158
O3210.99.312.620.6490.16314.174.266259.31.14e+0395.79.997.2464
O120.11126.12.320.2760.0040718.246.12.24e+0361.13.79e+0332238.40.00852218
C1_s20.11420.90.3560.3070.0053720.149.6NaNNaNNaNNaNNaNNaNNaN
C1_xNaNNaNNaNNaNNaNNaNNaNNaN2.24e+0361.13.79e+0332238.40.00852218
C1_tss17.827.944.990.53053043063043045.83e+03NaNNaNNaNNaNNaN
\n", "
" ], "text/plain": [ " S_O2 S_NH4 S_NO3 S_PO4 S_F S_A S_I S_ALK X_I X_S X_H X_PAO X_PP X_PHA X_AUT\n", "A1 0.00213 7.23 10.2 4.45 0.211 0.0265 15.9 67 2.28e+03 84.4 3.78e+03 322 37.2 0.0517 218\n", "A2 0.001 22.4 2.4 4.24 6.68 53.8 14.5 79 0 84.1 207 18.2 4.25 3.59 11.9\n", "O2 2 16.5 4.31 5.48 1.9 2.73 13.7 82.6 611 77.3 1.04e+03 86.4 6.45 11 58\n", "O3 2 10.9 9.31 2.62 0.649 0.163 14.1 74.2 662 59.3 1.14e+03 95.7 9.99 7.24 64\n", "O1 2 0.111 26.1 2.32 0.276 0.00407 18.2 46.1 2.24e+03 61.1 3.79e+03 322 38.4 0.00852 218\n", "C1_s 2 0.114 20.9 0.356 0.307 0.00537 20.1 49.6 NaN NaN NaN NaN NaN NaN NaN\n", "C1_x NaN NaN NaN NaN NaN NaN NaN NaN 2.24e+03 61.1 3.79e+03 322 38.4 0.00852 218\n", "C1_tss 17.8 27.9 44.9 90.5 305 304 306 304 304 5.83e+03 NaN NaN NaN NaN NaN" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df # Unlike other reactors, C1 has 3 rows for each of soluble, solids, and tss." ] }, { "cell_type": "code", "execution_count": 26, "id": "9920e00b", "metadata": { "execution": { "iopub.execute_input": "2026-05-29T13:10:54.786755Z", "iopub.status.busy": "2026-05-29T13:10:54.786755Z", "iopub.status.idle": "2026-05-29T13:10:54.790798Z", "shell.execute_reply": "2026-05-29T13:10:54.790798Z" } }, "outputs": [], "source": [ "# Create a function to set initial conditions of the reactors\n", "def batch_init(sys, df):\n", " dct = df.to_dict('index') # convert the DataFrame to a dictionary.\n", " u = sys.flowsheet.unit # unit registry (A1, A2, O1, O2, O3, C1)\n", " \n", " for k in [u.A1, u.A2, u.O1, u.O2, u.O3]: # for A1, A2, O1, O2, O3 reactor,\n", " k.set_init_conc(**dct[k._ID]) # A1.set_init_conc(**dct[k_ID]) \n", "\n", " c1s = {k:v for k,v in dct['C1_s'].items() if v>0} # for clarifier, need to use different methods\n", " c1x = {k:v for k,v in dct['C1_x'].items() if v>0}\n", " tss = [v for v in dct['C1_tss'].values() if v>0]\n", " u.C1.set_init_solubles(**c1s) # set solubles\n", " u.C1.set_init_sludge_solids(**c1x) # set sludge solids\n", " u.C1.set_init_TSS(tss) # set TSS" ] }, { "cell_type": "code", "execution_count": 27, "id": "f818d5e4", "metadata": { "execution": { "iopub.execute_input": "2026-05-29T13:10:54.794125Z", "iopub.status.busy": "2026-05-29T13:10:54.790798Z", "iopub.status.idle": "2026-05-29T13:10:54.798674Z", "shell.execute_reply": "2026-05-29T13:10:54.798674Z" } }, "outputs": [], "source": [ "# Set the initial conditions of the system\n", "batch_init(sys, df)" ] }, { "cell_type": "markdown", "id": "bd50264c", "metadata": {}, "source": [ "## 3. System simulation " ] }, { "cell_type": "markdown", "id": "9ddb7f02", "metadata": {}, "source": [ "### 3.1. Run simulation" ] }, { "cell_type": "markdown", "id": "e13-31-preface", "metadata": {}, "source": [ "The `solve_ivp` loop from [Tutorial 11](https://qsdsan.readthedocs.io/en/latest/tutorials/11_Dynamic_Simulation.html) now drives the entire flowsheet rather than a single reactor: at each integrator step, every dynamic unit's state derivative `_dstate` is evaluated and the global state vector is advanced. Because the flowsheet has two recycle loops (internal between O3 and A1, external between C1 and A1), `sys.simulate` repeats the full integration in an outer convergence loop until the recycle stream compositions satisfy `sys.set_tolerance(rmol=1e-6)`. The line `after 5 loops` in the system display below reports how many of those outer passes were needed." ] }, { "cell_type": "code", "execution_count": 28, "id": "55fc9f30", "metadata": { "execution": { "iopub.execute_input": "2026-05-29T13:10:54.801136Z", "iopub.status.busy": "2026-05-29T13:10:54.801136Z", "iopub.status.idle": "2026-05-29T13:10:54.804084Z", "shell.execute_reply": "2026-05-29T13:10:54.804084Z" } }, "outputs": [], "source": [ "# Simulation settings\n", "sys.set_dynamic_tracker(influent, effluent, A1, A2, O1, O2, O3, C1, wastage) # what you want to track changes in concentration\n", "sys.set_tolerance(rmol=1e-6)\n", "\n", "biomass_IDs = ('X_H', 'X_PAO', 'X_AUT')" ] }, { "cell_type": "code", "execution_count": 29, "id": "132152fe", "metadata": { "execution": { "iopub.execute_input": "2026-05-29T13:10:54.804084Z", "iopub.status.busy": "2026-05-29T13:10:54.804084Z", "iopub.status.idle": "2026-05-29T13:10:54.812683Z", "shell.execute_reply": "2026-05-29T13:10:54.810442Z" } }, "outputs": [], "source": [ "# Simulation settings\n", "t = 50 # total time for simulation\n", "t_step = 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": 30, "id": "74bcbaf0", "metadata": { "execution": { "iopub.execute_input": "2026-05-29T13:10:54.814915Z", "iopub.status.busy": "2026-05-29T13:10:54.814915Z", "iopub.status.idle": "2026-05-29T13:11:01.684173Z", "shell.execute_reply": "2026-05-29T13:11:01.683185Z" } }, "outputs": [], "source": [ "# Run simulation, this could take several minutes\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}.xlsx', # uncomment to export simulation result as excel file\n", " )" ] }, { "cell_type": "code", "execution_count": 31, "id": "952ea43e", "metadata": { "execution": { "iopub.execute_input": "2026-05-29T13:11:01.684173Z", "iopub.status.busy": "2026-05-29T13:11:01.684173Z", "iopub.status.idle": "2026-05-29T13:11:01.693568Z", "shell.execute_reply": "2026-05-29T13:11:01.693568Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Estimated SRT assuming at steady state is 10.02 days\n" ] } ], "source": [ "srt = get_SRT(sys, biomass_IDs)\n", "print(f'Estimated SRT assuming at steady state is {round(srt, 2)} days')" ] }, { "cell_type": "markdown", "id": "e13-srt-note", "metadata": {}, "source": [ "The estimated solids retention time (SRT) of about 10 days matches the typical design point for a BNR plant of this size, confirming that the recycle ratios and wastage flowrate set in §2.2 produce the intended sludge inventory at quasi-steady state." ] }, { "cell_type": "code", "execution_count": 32, "id": "55247c4c", "metadata": { "execution": { "iopub.execute_input": "2026-05-29T13:11:01.696050Z", "iopub.status.busy": "2026-05-29T13:11:01.696050Z", "iopub.status.idle": "2026-05-29T13:11:02.220601Z", "shell.execute_reply": "2026-05-29T13:11:02.219591Z" }, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "System: example_system\n", "Highest convergence error among components in recycle\n", "streams {C1-1, O3-0} after 5 loops:\n", "- flow rate 1.46e-11 kmol/hr (5.4e-14%)\n", "- temperature 0.00e+00 K (0%)\n", "ins...\n", "[0] influent \n", " phase: 'l', T: 293.15 K, P: 101325 Pa\n", " flow (kmol/hr): S_NH4 1.7\n", " S_NO3 0.0146\n", " S_PO4 0.0347\n", " S_F 15.4\n", " S_A 1.13\n", " S_I 10.8\n", " S_ALK 5.38\n", " ... 4.29e+04\n", "outs...\n", "[0] effluent \n", " phase: 'l', T: 293.15 K, P: 101325 Pa\n", " flow (kmol/hr): S_O2 0.000506\n", " S_N2 0.00716\n", " S_NH4 1.99\n", " S_NO3 9.69e-09\n", " S_PO4 0.0469\n", " S_F 0.973\n", " S_A 0.104\n", " ... 4.17e+04\n", "[1] wastage \n", " phase: 'l', T: 293.15 K, P: 101325 Pa\n", " flow (kmol/hr): S_O2 1.08e-05\n", " S_N2 0.000153\n", " S_NH4 0.0424\n", " S_NO3 2.07e-10\n", " S_PO4 0.000999\n", " S_F 0.0207\n", " S_A 0.00222\n", " ... 1.04e+03\n" ] } ], "source": [ "sys.show()" ] }, { "cell_type": "markdown", "id": "7b57f738", "metadata": {}, "source": [ "### 3.2. Check simulation results" ] }, { "cell_type": "markdown", "id": "e13-influent-note", "metadata": {}, "source": [ "
\n", "\n", "**Note.** The influent composition stays constant throughout this simulation (set once in §2.2 via `set_flow_by_concentration`), so its state-variable plot is flat. For time-varying inputs (diurnal patterns, step changes, file-driven profiles), see the `DynamicInfluent` walkthrough in [Tutorial 11 §3.2](https://qsdsan.readthedocs.io/en/latest/tutorials/11_Dynamic_Simulation.html#s3).\n", "\n", "
" ] }, { "cell_type": "code", "execution_count": 33, "id": "95af7a77", "metadata": { "execution": { "iopub.execute_input": "2026-05-29T13:11:02.220601Z", "iopub.status.busy": "2026-05-29T13:11:02.220601Z", "iopub.status.idle": "2026-05-29T13:11:02.668256Z", "shell.execute_reply": "2026-05-29T13:11:02.668256Z" } }, "outputs": [ { "data": { "text/plain": [ "(
,\n", " )" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Influent\n", "influent.scope.plot_time_series(\n", " ('S_I','X_I','S_F','S_A','X_S','S_NH4','S_N2','S_NO3','S_PO4',\n", " 'X_PP','X_PHA','X_H','X_AUT','X_PAO','S_ALK')\n", ")" ] }, { "cell_type": "code", "execution_count": 34, "id": "af021d39", "metadata": { "execution": { "iopub.execute_input": "2026-05-29T13:11:02.670276Z", "iopub.status.busy": "2026-05-29T13:11:02.670276Z", "iopub.status.idle": "2026-05-29T13:11:03.059390Z", "shell.execute_reply": "2026-05-29T13:11:03.059390Z" } }, "outputs": [ { "data": { "text/plain": [ "(
,\n", " )" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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CdwgjDBgwIH700UdX3XXXXWVz584t2nfffZOmFXvmmWeybrvttuzG1q8t7dyEE4AWqT/CrKKiyal9G1ReXt7qEcAAm5L6VWq0vcCm6Ne//nXOb37zm6REwYUXXlhx6qmnNj6sog3b0vrnsI1VUwDoCG677bbyeDy+tvYnjFZbuHBh0ZQpU0q/973vVSa2X6GE7t57793t9ddfb7S/sP7INuejQFcSqnJdeumldQ3dGWecUXnQQQcl3TBrLdf1wKZg1KhR1TvttFNK0+EMGjQoPm3atJK99947qb2dNGlSbm0Vm/q0pZ2bcALQIvXn+6mfVEtF/UoJ3bp182kANKF+O1m/ik0q6r+mqfnbADqayZMnZ1188cVJQ9fGjx9fedNNN6Vcardbt27x1rSlYR7MDa0ToKMKo9VCB/BRRx1V9cc//rHsnXfeKRoxYkRdx/GqVavCNA8FK1eubPD1hYWF8Q0FtlrQF6ANBTqE888/Py+0g8GWW24Zv/nmmzc4ZVhzua4HWF8IzN5///2lWVlfFUhctmxZLFRQ0JZ2PcIJQIvU7zwIpWxralIKwtWpn3rTIQHQvLa3sfRwY0I7Xf+mmrYX6CymTJmSdeaZZ+bH4181haEM5L333luWkZHR4g7h5ral9edGD50nKicAndWQIUPiU6dOLQmldWuf+/TTT2M33HBDborno80Oy5aUlBioAHQ4Dz/8cNaTTz5ZdxPs1ltvLevdu3ebb8d1PUDj56WHH354UkXE+tM5aku7BuEEoEU233zzeJh3rVZlZWX0xRdfNKtT4tNPP82ov04fB0DTbW/i46VLlzbrXO7zzz+PVVV9dY4fbuZpe4HOYOrUqZknn3xyfmIbNnr06OpHHnkkaWRFKuq3e4sXL25WW7pkyZKk5fv27escFujUQrt41VVXJZVAmDx5coNz/G6xxRbxptrEVHz22WdJfQdhdHJz1wHQ1n72s5/VTedw2GGHVZ1yyikpTRnWXK7rARo3evTopLZ33rx5DZ5raks7N+EEoMWlIAcOHJjUgbBw4cJmhRMWLVqUtPywYcOaV3oBYBMzdOjQmqba0Q2p306Hkr5G+wId3YwZMzKPPfbYgrKyr6rq7rPPPtVTpkwpyc1tcGBvk4YMGVLTmnBC/ba3ftsM0Bl9+9vfrkwcgBACBB9//PF655r15w5evHhxrLlh2cT2PCcnJ9p+++21o0C7W716dV17FsqIx2Kx7hv6GTt2bEH988T6y7z11ltJ55qu6wEaF/oqEx+HqR0aWk5b2rkJJwAtVr9jd86cOZnNef3cuXOTlt9pp52qfRwAjasf4vrwww+b1e5+8MEHScsPGTJEuwt0aO+8807GEUccUVBUVFT33G677Vbz9NNPl9SfniFVw4YNS2r7Pvjgg2ZdF8+ZM6fJDmaAziiULu/du3dSZ/Bnn32WsaHz0Y8//jijvDyp6EKz2tBtt922Jju7wSINAF2S63qAxmVnZyedj4aK3drSrkc4AWix3Xbbrbr+qLZUX7t06dJY4gje0Bmxyy676NgFaMKuu+5andh5G9rR0J6m+qa98sorSe10uMHnDQc6qhAaOPTQQwtWrlyZFAR49tlnS3r16tXi9Y4cOTKp7XvrrbcyG+vwaMiMGTOymjonBuiqncNBv3794onTMIRgwptvvplyX8DLL7/sfBTYpLmuB2hc/XBsY9PRaks7t+ZNzgmQ4Mgjj6y65ZZbcmofv/DCC1k1NTXr5jDfkGeffTap/TnwwAOru3fv7v0FaEKPHj2i/fffv/rFF1+s69R97rnnss4444wN3lUL7XNopxOfO+qoo9IyhyZAa4VS4occckhBYgnHbbbZJv7888+XtHZu8uHDh9dst9128QULFqxbd3Fx8bqbZQcffPAGQwahgsMbb7xR1waHEujaUqArWLNmTbRy5cqk0OtWW23VYHv7zW9+s+q+++6rS8w+99xzmQcccEBKQa1p06YlnY8eeeSRqafDANLor3/9a0lzAqvBO++8k3nppZfWzTO2xRZbxO+///7SpirPuq4HSH1g1YABAxo8H9WWdm7CCUCLhc6HzTbbLL58+fJYbSfyCy+8kDl27NgNdkrce++9SXUbjzrqKB0SACkGwxLDCaE9TSWcMG3atMxPPvkklthpsu+++xrtC3Q4oSLM2LFjCxMrw4SRulOnTi0eOHBgq4IJtY444ojK//qv/6oL2d5zzz3ZqYQTHnzwwezEKSZ23333msY6SwA6k7/97W9Z8fhXzVnfvn3joe1taNkQykoMJ9x///05V199dcWGBirMnz8/9tJLL9Wdx4aKYEcccYSwLNAhjB49utnXx1lZybdX8vLyonHjxm1wPa7rAdYXqiY+8cQTSfeNRo8e3ei5ora08zKtA9BimZmZ0fjx45NuiF177bW5YXRuU8KoisQEXKiYcMopp+iQAEjBqaeeWllYWFj3OIz2ff7555sspRva5dA+Jz53+umnV4Z2HKAjWb58eRQqJtRWNai9Qfbcc8+VbL/99m0WAjjrrLMqQ9WDWo899lj27Nmzm7w+Li0tjW666aa6QENwxhlnVLTVPgG0l5KSkuiaa65JOlf81re+VdXYuWKonNC/f/944lRjd999d1JHckN+8Ytf5CYGII455piq1kzTA9BZua4HWN+FF16Yt2rVqrrHOTk50eGHH97ofSNtaeclnAC0ymWXXVbRrVu3usdhFMT111+f1GmbaPHixbFzzjknP/G5H/zgBxWNzR0EQLJQXve8885LuhkW2tUlS5YkleFNdN111+Ukzu/bs2fP6NJLLy333gIdraT4uHHjCj/44IO669Rw0+qZZ54p2XnnnZtOvzbTiBEjao4//vi6To6Kiorou9/9bv7q1asbDXn9+Mc/zvvXv/5Vt2/bbrtt/Oyzz1b9C+gwLrzwwtzXXnsto7mhsCOOOKJg/vz5da8LoYQLL7yw0fBVGBlc/1zy0ksvzXv//fcb3fbkyZOzHnrooezEbVx77bXOR4FNkut6oCsL/ZBvvPFGyuekYUqdCy64IDexMldw9tlnVyQGYuvTlnZesXg8vqa9dwLo3H75y1/mXHXVVUmjLM4555zKK6+8sry2zG11dXUoyZP1k5/8JC8EFGqX23rrreOzZ88u6t27d3vsOkCbmz59emYYXduSuShrhRPvXXbZpaapTuSdd9652xdffFHXng4aNCj+n//5n2VHH310VW1J3UWLFsV++ctf5tYfyXb99deX//znPzfaF+hQvvGNbxSENjTxuauuuqp8v/32a3aJ3b322qu6T58+TS4zb9682MiRI7uFEcO1dt1115pbb721bMyYMXXb/PDDDzNC+z1lypSkur0PPfRQ6cknn6z6F9BhjBgxonDWrFkZe+65Z/UJJ5xQNWbMmKrQroVRZ/UDV6Fte+SRR7Juv/32nNqpGmtdcMEFFbfeemuTwYEQ6vra176WFCjr3bt3/JZbbik/7bTTKsOUDbXnrTfffHPur371q5zEKovnnntu5e9///uytjt6gI0vTJ84duzYgsTr8oULF341B1gTXNcDXdWBBx5YEAax7rPPPtXf/va3qw455JCqYcOG1dSeH9YKVRKeeuqprJtuuin3vffeSwozbLfddvHXX3+9OFRSbGpb2tLOSTgBaLUQPDjyyCPzn3766aQO2zASIpyU9+jRI5yYZySW5Any8/PXjYQ78MADzXkOdBmDBw/uFkIBrVlH6NCdPHlyk521f//73zO/9a1vFZSVJS8WRhkPHjy4ZvXq1bEQBgttdKIwr++UKVNKNzQnMMDGFovFurfVuqZOnVqSGDBozAMPPJA1fvz4/MQy40HoABk4cGB82bJlsaVLl8bq//7888+vuP322434BTpkOCHxuRBM6NevX7xnz57xnJyc+Nq1a0O7lrF27dpGz0Pvu+++slSm/wrT4YwaNapg5cqVSee+obritttuWxPOUz/55JOMMBouUQhP/OMf/ygpKKi7nwewyYUTAtf1QFcOJyQ+l5ubu24wVrhXFM4zV6xYEQtTgzU0RfiWW24ZD+eKQ4cOTamCora080m6kQjQEuE/k8cff7w0lMJ97LHH6tqVcEPs448/Dp0U692k69OnT/zRRx8tFUwAaJmDDz64+sknnyw56aST8hM7hEMQbNWqVQ0mD0466aSq++67TzAB4P985zvfqYrH46VhepzEqjdffvllLPw09EaFEcVhZLA3EegMQoWDTz75pMHr8kQ9evQIJXjLfvCDH1SmGmINU+6EMNixxx5bkBjOLSoqiuqHJBLPYR9//HHBBADX9cAmpLy8PFqwYMEGz0nHjRsX+i7LwpQNqa5bH2nnY8gc0CZCFYQQNnjkkUdKwxy+jS1XWFi4rnzj7Nmzi1MZzQZA4w455JDq0J6GqXSaGnm222671YQ2+uGHHy4NcwQD8JXTTjut6r333isKAa76ZSYTHXDAAdXTpk0rCaXOVZ8BOqIw3czEiRPLQwdtCBtsSCwWi8JUYpMmTSqfN29e0Y9+9KOUgwm1dt9995pZs2YVXXLJJRVNTde4ww471Pzud78rC2EG0zoCfMV1PdDVXHbZZeVnn312ZZjKIZVqXKHq1nHHHVf1wgsvlDzzzDOlzQkm1NKWdi6mdQDSIszh+9prr2UuWbIkI4zU6NWrV3z48OE1o0aNqg5BBgDaVpgz/eWXX86cM2dOmEYnFkr4DhgwoCbM7zZkyJBmn9QDbIpWr14dTZ8+PWvevHnrSp6HQFeYKicEE8I0D+29fwCpCpUMQ1s2f/78jFAyd82aNbEwvUL37t2jMMVDmHYhTK/Qs2fPNntTw7X/q6++mhmqJixfvjwWOqPDlBJ77LFHdQjL+vQAmua6HuhqiouLo/fffz8jTPX16aefxoqKitZN5RDuF4Xq2uGeUThPzMpqu0L/2tKOTzgBAAAAAAAAAEgr0zoAAAAAAAAAAGklnAAAAAAAAAAApJVwAgAAAAAAAACQVsIJAAA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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# However, the effluent does change over time, especially in the beginning of the simulation, as the system is not at steady state yet.\n", "effluent.scope.plot_time_series((('S_I','X_I','S_F','S_A','X_S','S_NH4','S_N2','S_NO3','S_PO4','X_PP','X_PHA',\n", " 'X_H','X_AUT','X_PAO','S_ALK'))) # you can plot how each state variable changes over time" ] }, { "cell_type": "code", "execution_count": 35, "id": "990d5e59", "metadata": { "execution": { "iopub.execute_input": "2026-05-29T13:11:03.061137Z", "iopub.status.busy": "2026-05-29T13:11:03.061137Z", "iopub.status.idle": "2026-05-29T13:11:03.270802Z", "shell.execute_reply": "2026-05-29T13:11:03.270802Z" } }, "outputs": [ { "data": { "text/plain": [ "(
,\n", " )" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "effluent.scope.plot_time_series(('S_NH4', 'S_NO3')) # you can plot how each state variable changes over time" ] }, { "cell_type": "code", "execution_count": 36, "id": "7aa98bac", "metadata": { "execution": { "iopub.execute_input": "2026-05-29T13:11:03.274531Z", "iopub.status.busy": "2026-05-29T13:11:03.270802Z", "iopub.status.idle": "2026-05-29T13:11:03.489850Z", "shell.execute_reply": "2026-05-29T13:11:03.489850Z" } }, "outputs": [ { "data": { "text/plain": [ "(
,\n", " )" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "effluent.scope.plot_time_series(('S_O2')) # you can plot how each state variable changes over time" ] }, { "cell_type": "markdown", "id": "e13-what-to-look-for", "metadata": {}, "source": [ "**What to look for.** A few features of these state trajectories are characteristic of a working BNR plant:\n", "\n", "- **S_NH4 down, S_NO3 up in the effluent.** Ammonium drops while nitrate rises in the aerated zones, the signature of nitrification by `X_AUT` (autotrophic biomass). The remaining nitrate is either recycled internally to the anoxic zones for denitrification, or leaves with the effluent.\n", "- **Dissolved oxygen tracks the KLa setting.** The effluent `S_O2` trajectory rises to a value set by the balance between aeration supply (`KLa * (DOsat - S_O2)` in Tank 5, where the effluent is drawn) and biological demand. This is the same exogenous-input pattern covered in [Tutorial 11 §3.1](https://qsdsan.readthedocs.io/en/latest/tutorials/11_Dynamic_Simulation.html#s3), applied per zone.\n", "- **Quasi-steady state by ~50 days.** Because all reactors started near a representative operating point (the initial conditions in §2.5.2), the simulation reaches a near-steady state within the 50-day window. A cold-start run would take several SRT (roughly 30 to 40 days here) to wash in.\n", "\n", "From here, you can change the influent composition, the KLa values, the recycle split, or the WAS flowrate, and re-run the simulation to see how each design parameter shifts effluent quality and SRT." ] }, { "cell_type": "markdown", "id": "nav-footer-13_process_modeling_101", "metadata": {}, "source": [ "\n", "\n", "---\n", "\n", "↑ Back to top\n" ] } ], "metadata": { "kernelspec": { "display_name": ".venv", "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": "3.12.7" }, "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 }