{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8d891055",
   "metadata": {},
   "source": [
    "# Anaerobic Digestion Model No. 1 (ADM1) <a class=\"anchor\" id=\"top\"></a>\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",
    "\n",
    "- **Learning objectives.** After this tutorial, you will be able to:\n",
    "\n",
    "    - Apply the ADM1 biokinetic model to anaerobic digestion\n",
    "    - Set up a multi-component dynamic system\n",
    "    - Analyze how operating conditions drive the digester response\n",
    "\n",
    "- **Prerequisites:** [11. Dynamic Simulation](https://qsdsan.readthedocs.io/en/latest/tutorials/11_Dynamic_Simulation.html)\n",
    "\n",
    "- **Covered topics:**\n",
    "\n",
    "    - <a href=\"#s1\">1. Introduction</a>\n",
    "    - <a href=\"#s2\">2. System setup</a>\n",
    "    - <a href=\"#s3\">3. Result discussion</a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f0ecf42bf56",
   "metadata": {},
   "source": [
    "<!-- tutorial-setup-section -->\n",
    "\n",
    "## Setup\n",
    "\n",
    "Import `QSDsan` and confirm the installed version. An ADM1 example system is also packaged ready-to-run in the `adm` module of [EXPOsan](https://github.com/QSD-Group/EXPOsan); this notebook builds an equivalent system from scratch so each piece is visible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9a2a96b7",
   "metadata": {
    "execution": {
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   },
   "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 <a class=\"anchor\" id=\"s1\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "t12-s11",
   "metadata": {},
   "source": [
    "In Tutorials 10 and 11, we have learned about the foundamentals of process modeling. As a practical example, this tutorial uses **ADM1**, the International Water Association (IWA)-validated benchmark for anaerobic digestion, as a substantial worked example that uses both at once.\n",
    "\n",
    "- [Tutorial 10](https://qsdsan.readthedocs.io/en/latest/tutorials/10_Process.html) covered how a kinetic model is represented in QSDsan: the `Process` and `Processes` classes that hold stoichiometry and rate equations, and the Petersen matrix that tabulates a whole process model. In this tutorial, we will use `ADM1`, which is a pre-constructed and ready-to-use `CompiledProcesses` object for ADM1 in QSDsan.\n",
    "\n",
    "- [Tutorial 11](https://qsdsan.readthedocs.io/en/latest/tutorials/11_Dynamic_Simulation.html) covered how a dynamic simulation is set up and run: the `AnaerobicCSTR` / `CompleteMixTank` family of dynamic units, the `_state` / `_dstate` methods, the integrator loop, and `set_dynamic_tracker` for following state evolution. In this tutorial, we will use `AnaerobicCSTR`, a dynamic `SanUnit`. We will also construct a dynamic `System` that contains `AnaerobicCSTR` for simulation."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "t12-s12-head",
   "metadata": {},
   "source": [
    "### 1.1. What ADM1 models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "t12-s12-body",
   "metadata": {},
   "source": [
    "ADM1 describes the biochemical and physico-chemical processes for anaerobic digestion (AD).\n",
    "\n",
    "The **biochemical steps** are organized as a cascade:\n",
    "\n",
    "- **Disintegration** of homogeneous particulates (`X_c`) into carbohydrates, proteins, and lipids.\n",
    "- **Hydrolysis** of those particulates to sugars (`S_su`), amino acids (`S_aa`), and long-chain fatty acids (LCFA, `S_fa`).\n",
    "- **Acidogenesis** of sugars and amino acids to volatile fatty acids (VFAs), acetate, and hydrogen.\n",
    "- **Acetogenesis** of LCFA and the longer VFAs (valerate, butyrate, propionate) to acetate and hydrogen.\n",
    "- **Methanogenesis** in two parallel paths: acetoclastic (`S_ac → S_ch4`) and hydrogenotrophic (`S_h2 → S_ch4`).\n",
    "\n",
    "Seven biomass groups catalyse these steps and decay back into the composite pool.\n",
    "\n",
    "The **physico-chemical equations** cover acid/base equilibria (which set the pH) and gas-liquid transfer for H₂, CH₄, and CO₂."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "t12-s12-fig",
   "metadata": {},
   "source": [
    "<img src=\"../images/adm1/adm1_network_light.svg\"\n",
    "     class=\"only-light\"\n",
    "     alt=\"ADM1 reaction network: four AD steps as horizontal bands from disintegration to methanogenesis, biomass groups catalysing each step, decay loop to composites.\"\n",
    "     width=\"640\">\n",
    "<img src=\"../images/adm1/adm1_network_dark.svg\"\n",
    "     class=\"only-dark\"\n",
    "     alt=\"ADM1 reaction network: four AD steps as horizontal bands from disintegration to methanogenesis, biomass groups catalysing each step, decay loop to composites.\"\n",
    "     width=\"640\">"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "t12-s12-cap",
   "metadata": {},
   "source": [
    "*The four canonical AD steps as represented in ADM1. Each band corresponds to one step; the biomass group labelled on each arrow catalyses that conversion.*\n",
    "\n",
    "Reference: Batstone, D. J., et al. (2002). The IWA Anaerobic Digestion Model No. 1 (ADM1). *Water Science and Technology*, 45(10), 65&ndash;73. https://doi.org/10.2166/wst.2002.0292"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "t12-s13-head",
   "metadata": {},
   "source": [
    "### 1.2. Differential-algebraic structure"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "t12-s13-body",
   "metadata": {},
   "source": [
    "Implemented as a **differential-algebraic (DAE) set**, ADM1 has 26 dynamic state variables in the liquid phase and 8 implicit algebraic variables in the headspace (the partial pressures of H₂, CH₄, CO₂, and H₂O, plus the ion balance that sets pH). Implemented as pure ODEs, there are 32 dynamic variables. The figure below shows the DAE shape that `AnaerobicCSTR` evaluates at each integrator step."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "t12-s13-fig",
   "metadata": {},
   "source": [
    "<img src=\"../images/adm1/adm1_dae_light.svg\"\n",
    "     class=\"only-light\"\n",
    "     alt=\"DAE structure of AnaerobicCSTR: liquid-phase ODE block (26 state variables), headspace algebraic block (partial pressures plus ion balance), and gas-liquid transfer terms linking them.\"\n",
    "     width=\"820\">\n",
    "<img src=\"../images/adm1/adm1_dae_dark.svg\"\n",
    "     class=\"only-dark\"\n",
    "     alt=\"DAE structure of AnaerobicCSTR: liquid-phase ODE block (26 state variables), headspace algebraic block (partial pressures plus ion balance), and gas-liquid transfer terms linking them.\"\n",
    "     width=\"820\">"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "t12-s13-cap",
   "metadata": {},
   "source": [
    "*Each integrator step evaluates the liquid-phase ODE block, the headspace algebraic block, and the gas-liquid transfer terms (`k_L·a · (S_aq − K_H · p)`) that link them. Liquid effluent and biogas leave through outflow arrows; the biomass-decay loop (not drawn here) returns active biomass to `X_c` in the ODE block.*\n",
    "\n",
    "<div class=\"alert alert-info\">\n",
    "\n",
    "**Note on `algebraic_h2`.** Hydrogen has very fast dynamics relative to the rest of the model (small pool, rapid turnover), which makes the ODE block stiff. `AnaerobicCSTR` exposes an `algebraic_h2` switch: with `algebraic_h2=True` the `S_h2` equation is moved into the algebraic block, removing this source of stiffness at the cost of one extra algebraic equation. The default `algebraic_h2=False` keeps `S_h2` as an ODE, which is what we use throughout this tutorial.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "deab6410",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "\n",
    "**Note:** You can find validation of the ADM1 system in [EXPOsan](https://github.com/QSD-Group/EXPOsan/tree/main/exposan/adm).\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fb4e6486",
   "metadata": {
    "execution": {
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   "source": [
    "# Import packages\n",
    "import numpy as np\n",
    "from chemicals.elements import molecular_weight as get_mw\n",
    "from qsdsan import unit_operations as su, process_models as pc, WasteStream, System\n",
    "from qsdsan.utils import time_printer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c7244dc",
   "metadata": {},
   "source": [
    "### 1.3. State variables of ADM1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "t12-s21-link",
   "metadata": {},
   "source": [
    "Components were introduced in [Tutorial 2](https://qsdsan.readthedocs.io/en/latest/tutorials/2_Component.html); `pc.create_adm1_cmps` assembles the ADM1-specific set in one call so we don't have to define each of the 27 components by hand."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5774fdae",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2026-05-28T19:09:09.616200Z"
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CompiledComponents([\n",
      "    S_su,  S_aa,  S_fa, S_va,\n",
      "    S_bu,  S_pro, S_ac, S_h2,\n",
      "    S_ch4, S_IC,  S_IN, S_I, \n",
      "    X_c,   X_ch,  X_pr, X_li,\n",
      "    X_su,  X_aa,  X_fa, X_c4,\n",
      "    X_pro, X_ac,  X_h2, X_I, \n",
      "    S_cat, S_an,  H2O, \n",
      "])\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": [
    "The 26 ADM1 state variables (plus water, which is always present):\n",
    "\n",
    "- **S_su**: monosaccharides\n",
    "- **S_aa**: amino acids\n",
    "- **S_fa**: long-chain fatty acids (LCFA)\n",
    "- **S_va**: valerate\n",
    "- **S_bu**: butyrate\n",
    "- **S_pro**: propionate\n",
    "- **S_ac**: acetate\n",
    "- **S_h2**: dissolved hydrogen\n",
    "- **S_ch4**: dissolved methane\n",
    "- **S_IC**: inorganic carbon (dissolved CO₂ and HCO₃⁻)\n",
    "- **S_IN**: inorganic nitrogen\n",
    "- **S_I**: soluble inerts\n",
    "- **X_c**: composites\n",
    "- **X_ch**: carbohydrates\n",
    "- **X_pr**: proteins\n",
    "- **X_li**: lipids\n",
    "- **X_su**: biomass that takes up sugars\n",
    "- **X_aa**: biomass that takes up amino acids\n",
    "- **X_fa**: biomass that takes up LCFA\n",
    "- **X_c4**: biomass that takes up C4 fatty acids (valerate and butyrate)\n",
    "- **X_pro**: biomass that takes up propionate\n",
    "- **X_ac**: biomass that takes up acetate\n",
    "- **X_h2**: biomass that takes up hydrogen\n",
    "- **X_I**: particulate inerts\n",
    "- **S_cat**: other cations\n",
    "- **S_an**: other anions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4f28ea2",
   "metadata": {},
   "source": [
    "### 1.4. The ADM1 `Process`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "t12-s22-link",
   "metadata": {},
   "source": [
    "As covered in [Tutorial 10](https://qsdsan.readthedocs.io/en/latest/tutorials/10_Process.html), `Processes.load_from_file` lets you build a process model from a spreadsheet. `pc.ADM1()` is the prebuilt variant: the same `CompiledProcesses` class, with the IWA-specified rate equations and parameters already assembled."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0dd6a5b8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-28T19:09:09.619811Z",
     "iopub.status.busy": "2026-05-28T19:09:09.619811Z",
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     "shell.execute_reply": "2026-05-28T19:09:11.507562Z"
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   },
   "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": [
    "### 1.5. Petersen matrix of ADM1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "t12-s23-link",
   "metadata": {},
   "source": [
    "As introduced in [Tutorial 10](https://qsdsan.readthedocs.io/en/latest/tutorials/10_Process.html), the table below is the **Petersen matrix** of ADM1: 22 rows (processes) by 27 columns (components plus water). Each cell is a stoichiometric coefficient."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9a9db08e",
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   "outputs": [
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>uptake_acetate</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>uptake_h2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>decay_Xsu</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>decay_Xaa</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>decay_Xfa</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>decay_Xc4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>decay_Xpro</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>decay_Xac</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>decay_Xh2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h2_transfer</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ch4_transfer</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IC_transfer</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22 rows × 27 columns</p>\n",
       "</div>"
      ],
      "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**<br>\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) <br>\n",
    "$\\rho_i$: process $i$'s reaction rate <br>\n",
    "$r_j$:  the overall production or consumption rate of component $j$<br>\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": "8388e5be",
   "metadata": {},
   "source": [
    "## 2. System setup <a class=\"anchor\" id=\"s2\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2c2360d",
   "metadata": {},
   "source": [
    "### 2.1. Influent & effluent"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43d0eba8",
   "metadata": {},
   "source": [
    "For illustrative purposes, let's assume the following setup for the system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "28a9c8e5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-28T19:09:11.784833Z",
     "iopub.status.busy": "2026-05-28T19:09:11.783793Z",
     "iopub.status.idle": "2026-05-28T19:09:11.793249Z",
     "shell.execute_reply": "2026-05-28T19:09:11.793249Z"
    }
   },
   "outputs": [],
   "source": [
    "Temp = 273.15+35                                    # temperature [K]\n",
    "inf = WasteStream('Influent', T=Temp)               # influent\n",
    "eff = WasteStream('Effluent', T=Temp)               # effluent\n",
    "gas = WasteStream('Biogas')                         # gas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bdd90569",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-28T19:09:11.795849Z",
     "iopub.status.busy": "2026-05-28T19:09:11.795849Z",
     "iopub.status.idle": "2026-05-28T19:09:12.312475Z",
     "shell.execute_reply": "2026-05-28T19:09:12.312475Z"
    }
   },
   "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",
      "             ...    6.99e+06\n",
      " WasteStream-specific properties:\n",
      "  pH         : 7.0\n",
      "  Alkalinity : 2.5 mmol/L\n",
      "  COD        : 57096.0 mg/L\n",
      "  BOD        : 12769.4 mg/L\n",
      "  TC         : 20581.4 mg/L\n",
      "  TOC        : 20101.0 mg/L\n",
      "  TN         : 3681.4 mg/L\n",
      "  TP         : 489.3 mg/L\n",
      "  TK         : 9.8 mg/L\n",
      "  TSS        : 32679.7 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 flowrate and 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",
    "Q = 170                                             # influent flowrate [m3/d]\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": [
    "### 2.2. Reactor"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "t12-s25-link",
   "metadata": {},
   "source": [
    "`AnaerobicCSTR` is the same kind of dynamic unit as the `CompleteMixTank` from [Tutorial 11](https://qsdsan.readthedocs.io/en/latest/tutorials/11_Dynamic_Simulation.html); it adds the headspace algebraic block shown above."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f572686d",
   "metadata": {},
   "source": [
    "The key parameters of `AnaerobicCSTR` are:\n",
    "\n",
    "- **ins**: influent `WasteStream` to the reactor.\n",
    "- **outs**: the biogas and the treated effluent(s).\n",
    "- **model**: process model used in the reactor.\n",
    "- **HRT**: hydraulic retention time [d].\n",
    "- **V_liq**: liquid-phase volume [m^3].\n",
    "- **V_gas**: headspace volume [m^3].\n",
    "- **model**: the kinetic model (typically ADM1-like).\n",
    "- **T**: operating temperature [K].\n",
    "\n",
    "For the complete signature and all parameters (e.g., `headspace_P`, `retain_cmps`, `fraction_retain`), run `?su.AnaerobicCSTR` or see the [unit operations API](https://qsdsan.readthedocs.io/en/latest/api/unit_operations/index.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1fc90df0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-28T19:09:12.314481Z",
     "iopub.status.busy": "2026-05-28T19:09:12.314481Z",
     "iopub.status.idle": "2026-05-28T19:09:12.319585Z",
     "shell.execute_reply": "2026-05-28T19:09:12.319071Z"
    }
   },
   "outputs": [],
   "source": [
    "# Assume an HRT of 5 days, and the gas volume is 10% of the liquid volume in the reactor\n",
    "HRT = 5\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": "code",
   "execution_count": 9,
   "id": "4d403072",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-28T19:09:12.321629Z",
     "iopub.status.busy": "2026-05-28T19:09:12.320609Z",
     "iopub.status.idle": "2026-05-28T19:09:13.224296Z",
     "shell.execute_reply": "2026-05-28T19:09:13.224296Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "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",
      "                ...    6.99e+06\n",
      "    WasteStream-specific properties:\n",
      "     pH         : 7.0\n",
      "     Alkalinity : 2.5 mmol/L\n",
      "     COD        : 57096.0 mg/L\n",
      "     BOD        : 12769.4 mg/L\n",
      "     TC         : 20581.4 mg/L\n",
      "     TOC        : 20101.0 mg/L\n",
      "     TN         : 3681.4 mg/L\n",
      "     TP         : 489.3 mg/L\n",
      "     TK         : 9.8 mg/L\n",
      "     TSS        : 32679.7 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": [
    "# anaerobic CSTR with influent, effluent, and biogas\n",
    "# before running the simulation, 'outs' have nothing\n",
    "AD.show()           "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b162ac79",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-28T19:09:13.224296Z",
     "iopub.status.busy": "2026-05-28T19:09:13.224296Z",
     "iopub.status.idle": "2026-05-28T19:09:13.230479Z",
     "shell.execute_reply": "2026-05-28T19:09:13.230479Z"
    }
   },
   "outputs": [],
   "source": [
    "# Set the initial condition of the reactor,\n",
    "# the closer the intial condition is to the steady state,\n",
    "# the faster the simulation will converge.\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",
    "    }\n",
    "\n",
    "AD.set_init_conc(**default_init_conds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "051f6b47",
   "metadata": {},
   "source": [
    "### 2.3. System"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "t12-s26-link",
   "metadata": {},
   "source": [
    "As in [Tutorial 11](https://qsdsan.readthedocs.io/en/latest/tutorials/11_Dynamic_Simulation.html), `set_dynamic_tracker` decides which objects retain their state-trajectory history. Here we pass the two outlet `WasteStream`s (`eff` and `gas`), so `eff.scope` and `gas.scope` are populated after the simulation, which is what §3 will plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "85b13876",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-28T19:09:13.230479Z",
     "iopub.status.busy": "2026-05-28T19:09:13.230479Z",
     "iopub.status.idle": "2026-05-28T19:09:13.756970Z",
     "shell.execute_reply": "2026-05-28T19:09:13.756970Z"
    }
   },
   "outputs": [
    {
     "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   0.000369\n",
      "                    S_aa   0.00708\n",
      "                    S_fa   9.62e-06\n",
      "                    S_va   3.41e-05\n",
      "                    S_bu   4.43e-05\n",
      "                    S_pro  6.32e-05\n",
      "                    S_ac   0.000111\n",
      "                    ...    710\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": [
    "# We need to include the reactor in a system to run the dynamic simulation,\n",
    "# and we need to tell the system what we want to track.\n",
    "sys = System('Anaerobic_Digestion', path=(AD,))                 # aggregation of unit operations\n",
    "sys.set_dynamic_tracker(eff, gas)                               # what you want to track changes in concentration\n",
    "sys.show()                                                      # before running the simulation, 'outs' have nothing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "132152fe",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-28T19:09:13.756970Z",
     "iopub.status.busy": "2026-05-28T19:09:13.756970Z",
     "iopub.status.idle": "2026-05-28T19:09:13.765600Z",
     "shell.execute_reply": "2026-05-28T19:09:13.764269Z"
    }
   },
   "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",
    "# See more info about the integration methods here:\n",
    "# https://docs.scipy.org/doc/scipy/reference/generated/scipy.integrate.solve_ivp.html\n",
    "method = 'BDF'                  # integration method to use\n",
    "# method = 'RK45'\n",
    "# method = 'RK23'\n",
    "# method = 'DOP853'\n",
    "# method = 'Radau'\n",
    "# method = 'LSODA'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "74bcbaf0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-28T19:09:13.768117Z",
     "iopub.status.busy": "2026-05-28T19:09:13.767112Z",
     "iopub.status.idle": "2026-05-28T19:09:14.192980Z",
     "shell.execute_reply": "2026-05-28T19:09:14.191971Z"
    }
   },
   "outputs": [],
   "source": [
    "# Run simulation, you can use `export_state_to` to export the simulation result as an excel file.\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",
    "            )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "55247c4c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-28T19:09:14.194981Z",
     "iopub.status.busy": "2026-05-28T19:09:14.194981Z",
     "iopub.status.idle": "2026-05-28T19:09:14.704541Z",
     "shell.execute_reply": "2026-05-28T19:09:14.703530Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "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   0.000369\n",
      "                    S_aa   0.00708\n",
      "                    S_fa   9.62e-06\n",
      "                    S_va   3.41e-05\n",
      "                    S_bu   4.43e-05\n",
      "                    S_pro  6.32e-05\n",
      "                    S_ac   0.000111\n",
      "                    ...    710\n",
      "outs...\n",
      "[0] Biogas  \n",
      "    phase: 'g', T: 308.15 K, P: 101325 Pa\n",
      "    flow (kmol/hr): S_h2   0.000151\n",
      "                    S_ch4  2.13\n",
      "                    S_IC   1.51\n",
      "                    H2O    0.205\n",
      "[1] Effluent  \n",
      "    phase: 'l', T: 308.15 K, P: 101325 Pa\n",
      "    flow (kmol/hr): S_su   0.00154\n",
      "                    S_aa   0.129\n",
      "                    S_fa   0.00772\n",
      "                    S_va   0.00163\n",
      "                    S_bu   0.00243\n",
      "                    S_pro  0.00691\n",
      "                    S_ac   0.59\n",
      "                    ...    587\n"
     ]
    }
   ],
   "source": [
    "# Now you have 'outs' info.\n",
    "sys.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e12-transient-note",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "\n",
    "**Note:** `t = 10` days only captures the early transient &mdash; the digester hasn't yet reached its long-term operating state. §3 below walks through what the response looks like in this window and what it tells us about the digester's behaviour.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "deb7de4a",
   "metadata": {},
   "source": [
    "## 3. Result discussion <a class=\"anchor\" id=\"s3\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b57f738",
   "metadata": {},
   "source": [
    "### 3.1. Effluent"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "t12-s31-vfa-disc",
   "metadata": {},
   "source": [
    "The VFA traces show three different behaviours:\n",
    "\n",
    "- **Acetate (`S_ac`)** climbs from ~90 to ~5300 mg/L over the 10-day window, but the rate of accumulation slows substantially toward the end (~1100 mg/L/d around day 1 drops to ~250 mg/L/d around day 10): the curve is approaching a plateau where the high acetate concentration pushes the acetoclastic methanogens (`X_ac`) toward their maximum uptake rate, so methanogenesis eventually catches up with what acetogenesis releases. The plateau sits well above a healthy AD operating range (typical effluent `S_ac` is <100 mg/L), so the digester is still in an overloaded state.\n",
    "- **Long-chain fatty acids (`S_fa`)** rise steadily from ~100 to ~800 mg/L. Hydrolysis of lipids produces LCFA faster than the LCFA-uptaking acetogens (`X_fa`) can consume them.\n",
    "- **Valerate (`S_va`), butyrate (`S_bu`), propionate (`S_pro`), and amino acids (`S_aa`)** overshoot in the first few days then partially decline as `X_c4`, `X_pro`, and `X_aa` grow into the substrate pulse. `S_pro` peaks around ~260 mg/L near day 3 before settling toward ~110 mg/L.\n",
    "\n",
    "This is a classic AD start-up imbalance: the upstream steps (hydrolysis, acidogenesis, acetogenesis of short VFAs) are fast, but LCFA degradation and acetoclastic methanogenesis are slow. Without intervention (lower organic load, biomass retention, longer HRT), a real digester operating in this regime would not produce a useful effluent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "990d5e59",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-28T19:09:14.707541Z",
     "iopub.status.busy": "2026-05-28T19:09:14.706540Z",
     "iopub.status.idle": "2026-05-28T19:09:14.979838Z",
     "shell.execute_reply": "2026-05-28T19:09:14.979838Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 2400x1350 with 1 Axes>,\n",
       " <Axes: xlabel='Time [d]', ylabel='Concentration [mg/L]'>)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2400x1350 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Track how each state variable changes over time\n",
    "eff.scope.plot_time_series(('S_aa', 'S_fa', 'S_va', 'S_bu', 'S_pro', 'S_ac'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "t12-s31-ic-disc",
   "metadata": {},
   "source": [
    "`S_IC` (inorganic carbon: dissolved CO₂ and HCO₃⁻) drops during this transient, from ~1140 to ~780 mg/L. The influent carries only ~480 mg/L `S_IC`, so at HRT = 5 d the dilution outflow pulls the liquid-phase `S_IC` down faster than the still-ramping-up methanogens can replenish it (acetoclastic methanogenesis is the main biological CO₂ source)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6f674fab",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-28T19:09:14.979838Z",
     "iopub.status.busy": "2026-05-28T19:09:14.979838Z",
     "iopub.status.idle": "2026-05-28T19:09:15.165285Z",
     "shell.execute_reply": "2026-05-28T19:09:15.165285Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 2400x1350 with 1 Axes>,\n",
       " <Axes: xlabel='Time [d]', ylabel='Concentration [mg/L]'>)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x1350 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eff.scope.plot_time_series(('S_IC'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "t12-s31-bio-disc",
   "metadata": {},
   "source": "The six biomass groups split into two camps in this 10-day window:\n\n- **Growing:** `X_aa` (+50%), `X_c4` (+55%), `X_pro` (+54%), `X_h2` (+25%). Their substrates are abundant and their net growth rate exceeds the dilution rate (1/HRT = 0.2 d⁻¹).\n- **Declining:** `X_fa` (&minus;26%) and `X_ac` (&minus;7%). These two are the slowest growers in ADM1, and on top of that their net growth is reduced further by pH inhibition as VFAs accumulate. Even with plenty of substrate they can't sustain themselves &mdash; which is exactly why `S_fa` and `S_ac` keep climbing in the plot above.\n\nThis split is the fundamental reason high-rate AD reactors decouple solids retention time (SRT) from hydraulic retention time (HRT): granular sludge, packed beds, and membrane systems all retain `X_fa` and `X_ac` against washout. See [exposan.metab](https://github.com/QSD-Group/EXPOsan/tree/main/exposan/metab) for examples."
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2ea79de8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-28T19:09:15.167318Z",
     "iopub.status.busy": "2026-05-28T19:09:15.167318Z",
     "iopub.status.idle": "2026-05-28T19:09:15.392827Z",
     "shell.execute_reply": "2026-05-28T19:09:15.392827Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 2400x1350 with 1 Axes>,\n",
       " <Axes: xlabel='Time [d]', ylabel='Concentration [mg/L]'>)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x1350 with 1 Axes>"
      ]
     },
     "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. Gas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "t12-s32-h2-disc",
   "metadata": {},
   "source": [
    "Headspace H₂ significantly decreased within the first day or two (the initial value reflects the default headspace initialisation, not a steady state) and then stays at very low partial pressures throughout the rest of the window. The hydrogenotrophic methanogens (`X_h2`) consume H₂ about as fast as it's produced, and the resulting low `p_H2` (< 1 mbar) is thermodynamically required for the upstream acetogenic steps &mdash; propionate and butyrate degradation are only exergonic at very low `p_H2`. `X_h2` grows steadily through this window (+25%), confirming the tight coupling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d54aeb58",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-28T19:09:15.397373Z",
     "iopub.status.busy": "2026-05-28T19:09:15.395955Z",
     "iopub.status.idle": "2026-05-28T19:09:15.570207Z",
     "shell.execute_reply": "2026-05-28T19:09:15.569199Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 2400x1350 with 1 Axes>,\n",
       " <Axes: xlabel='Time [d]', ylabel='Concentration [mg/L]'>)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x1350 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gas.scope.plot_time_series(('S_h2'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "t12-s32-ch4-disc",
   "metadata": {},
   "source": [
    "Headspace methane rises sharply in the first few days, peaks around day 3 at ~1830 mg/L, then declines back toward ~1460 mg/L as the `X_ac` population thins out: the digester is producing acetate faster than it can be converted to methane (consistent with the runaway `S_ac` in §3.1). CO₂ in the headspace follows a similar peak-and-decline pattern at much lower concentration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e021d8fb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-28T19:09:15.571216Z",
     "iopub.status.busy": "2026-05-28T19:09:15.571216Z",
     "iopub.status.idle": "2026-05-28T19:09:15.761473Z",
     "shell.execute_reply": "2026-05-28T19:09:15.761473Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 2400x1350 with 1 Axes>,\n",
       " <Axes: xlabel='Time [d]', ylabel='Concentration [mg/L]'>)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x1350 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gas.scope.plot_time_series(('S_ch4','S_IC'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "t12-s35-head",
   "metadata": {},
   "source": [
    "### 3.3. Extensions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "t12-s35-body",
   "metadata": {},
   "source": "Relevant contents in QSDsan:\n\n- **The validation system itself.** [exposan.adm](https://github.com/QSD-Group/EXPOsan/tree/main/exposan/adm) packages exactly this digester as a one-call `load()`; its test suite (`EXPOsan/tests/test_adm.py`) verifies the 26 state variables and the four major biogas species against the published steady state.\n- **ADM1 variants.** [exposan.adm1_p_ext](https://github.com/QSD-Group/EXPOsan/tree/main/exposan/adm1_p_ext) adds phosphorus to the model. [exposan.metab](https://github.com/QSD-Group/EXPOsan/tree/main/exposan/metab) wraps ADM1 inside granular-sludge, packed-bed, and membrane-degassing reactor variants for high-rate anaerobic treatment.\n- **Full-plant context.** [exposan.bsm2](https://github.com/QSD-Group/EXPOsan/tree/main/exposan/bsm2) is the BSM2 benchmark: a full wastewater treatment plant where an ADM1 digester sits downstream of an activated-sludge process, with recycle loops, sludge thickeners, and so on. [exposan.werf](https://github.com/QSD-Group/EXPOsan/tree/main/exposan/werf) bundles seven WERF flowsheet variants (B1, C1, F1, G1, H1, I1, N1) where an `AnaerobicCSTR` running ADM1p (the phosphorus-extended variant) digests waste sludge from an mASM2d activated-sludge process, with ASM↔ADM interface units handling the component translation.\n- **Parameter studies.** The uncertainty and sensitivity patterns from [Tutorial 9](https://qsdsan.readthedocs.io/en/latest/tutorials/9_Uncertainty_and_Sensitivity_Analyses.html) apply directly: you can wrap the `sys` here in a `qs.Model`, sample over ADM1 parameters (`adm1.set_parameters(...)`) or operating conditions (T, HRT, influent composition), and propagate through to biogas yield and effluent VFAs."
  },
  {
   "cell_type": "markdown",
   "id": "nav-footer-12_anaerobic_digestion_model_no_1",
   "metadata": {},
   "source": [
    "<!-- tutorial-nav-footer -->\n",
    "\n",
    "---\n",
    "\n",
    "<a href=\"#top\">↑ Back to top</a>\n"
   ]
  }
 ],
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