diff --git a/docs/source/api/sanunits/_index.rst b/docs/source/api/sanunits/_index.rst
index 1c1eab87..72aa4069 100644
--- a/docs/source/api/sanunits/_index.rst
+++ b/docs/source/api/sanunits/_index.rst
@@ -37,7 +37,6 @@ Individual Unit Operations
    CropApplication
    DynamicInfluent
    ElectrochemicalCell
-   encapsulation_bioreactor
    Excretion
    Flash
    heat_exchanging
diff --git a/docs/source/api/sanunits/encapsulation_bioreactor.rst b/docs/source/api/sanunits/encapsulation_bioreactor.rst
deleted file mode 100644
index bc4d9476..00000000
--- a/docs/source/api/sanunits/encapsulation_bioreactor.rst
+++ /dev/null
@@ -1,4 +0,0 @@
-Encapsulation Bioreactor
-========================
-.. automodule:: qsdsan.sanunits._encapsulation_bioreactor
-    :members:
\ No newline at end of file
diff --git a/docs/source/tutorials/0_Quick_Overview.ipynb b/docs/source/tutorials/0_Quick_Overview.ipynb
index ed9e66f4..e8191de7 100644
--- a/docs/source/tutorials/0_Quick_Overview.ipynb
+++ b/docs/source/tutorials/0_Quick_Overview.ipynb
@@ -9,7 +9,7 @@
     "\n",
     "- **Prepared by:**\n",
     "    \n",
-    "    - [Yalin Li](https://qsdsan.readthedocs.io/en/latest/authors/Yalin_Li.html)\n",
+    "    - [Yalin Li](https://qsdsan.readthedocs.io/en/latest/CONTRIBUTING.html)\n",
     "    \n",
     "To run tutorials in your browser, go to this [Binder page](https://mybinder.org/v2/gh/QSD-Group/QSDsan/main?filepath=%2Fdocs%2Fsource%2Ftutorials)."
    ]
diff --git a/docs/source/tutorials/10_Process.ipynb b/docs/source/tutorials/10_Process.ipynb
index 7cc73a09..73b8659b 100644
--- a/docs/source/tutorials/10_Process.ipynb
+++ b/docs/source/tutorials/10_Process.ipynb
@@ -9,7 +9,7 @@
     "\n",
     "- **Prepared by:**\n",
     "    \n",
-    "    - [Joy Zhang](https://qsdsan.readthedocs.io/en/latest/authors/Joy_Zhang.html)\n",
+    "    - [Joy Zhang](https://qsdsan.readthedocs.io/en/latest/CONTRIBUTING.html)\n",
     "    \n",
     "- **Covered topics:**\n",
     "\n",
@@ -3001,9 +3001,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python [conda env:tut]",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "conda-env-tut-py"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -3015,7 +3015,36 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.0"
+   "version": "3.9.13"
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
   }
  },
  "nbformat": 4,
diff --git a/docs/source/tutorials/11_Dynamic_Simulation.ipynb b/docs/source/tutorials/11_Dynamic_Simulation.ipynb
index aed3ea92..ba9c9bbe 100644
--- a/docs/source/tutorials/11_Dynamic_Simulation.ipynb
+++ b/docs/source/tutorials/11_Dynamic_Simulation.ipynb
@@ -9,7 +9,7 @@
     "\n",
     "- **Prepared by:**\n",
     "    \n",
-    "    - [Joy Zhang](https://qsdsan.readthedocs.io/en/latest/authors/Joy_Zhang.html)\n",
+    "    - [Joy Zhang](https://qsdsan.readthedocs.io/en/latest/CONTRIBUTING.html)\n",
     "    \n",
     "- **Covered topics:**\n",
     "\n",
@@ -2168,9 +2168,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python [conda env:tut]",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "conda-env-tut-py"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -2182,7 +2182,36 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.0"
+   "version": "3.9.13"
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
   }
  },
  "nbformat": 4,
diff --git a/docs/source/tutorials/12_Anaerobic_Digestion_Model_No_1.ipynb b/docs/source/tutorials/12_Anaerobic_Digestion_Model_No_1.ipynb
new file mode 100644
index 00000000..4375a072
--- /dev/null
+++ b/docs/source/tutorials/12_Anaerobic_Digestion_Model_No_1.ipynb
@@ -0,0 +1,2297 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "8d891055",
+   "metadata": {},
+   "source": [
+    "# Anaerobic Digestion Model No. 1 (ADM1) <a class=\"anchor\" id=\"top\"></a>\n",
+    "\n",
+    "- **Prepared by:**\n",
+    "    \n",
+    "    - [Ga-Yeong Kim](https://qsdsan.readthedocs.io/en/latest/CONTRIBUTING.html)\n",
+    "    \n",
+    "- **Covered topics:**\n",
+    "\n",
+    "    - [1. Introduction](#s1)\n",
+    "    - [2. System Setup](#s2)\n",
+    "    - [3. System Simulation](#s3)\n",
+    "    \n",
+    "- **Video demo:**\n",
+    "\n",
+    "    - To be posted\n",
+    "    \n",
+    "To run tutorials in your browser, go to this [Binder page](https://mybinder.org/v2/gh/QSD-Group/QSDsan/main?filepath=%2Fdocs%2Fsource%2Ftutorials).\n",
+    "    \n",
+    "You can also watch a video demo on YouTube (link to be posted) (subscriptions & likes appreciated!)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "9a2a96b7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This tutorial was made with qsdsan v1.3.0 and exposan v1.3.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "import qsdsan as qs, exposan\n",
+    "print(f'This tutorial was made with qsdsan v{qs.__version__} and exposan v{exposan.__version__}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1bbdffaa",
+   "metadata": {},
+   "source": [
+    "## 1. Introduction <a class=\"anchor\" id=\"s1\"></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cefa6e0a",
+   "metadata": {},
+   "source": [
+    "Anaerobic Digestion Model No.1 (ADM1) includes multiple steps describing **biochemical** as well as **physicochemical processes**. \n",
+    "\n",
+    "The **biochemical steps** include disintegration from homogeneous particulates to carbohydrates, proteins and lipids; extracellular hydrolysis of these particulate substrates to sugars, amino acids, and long chain fatty acids (LCFA), respectively; acidogenesis from sugars and amino acids to volatile fatty acids (VFAs) and hydrogen; acetogenesis of LCFA and VFAs to acetate; and separate methanogenesis steps from acetate and hydrogen/CO2. \n",
+    "\n",
+    "The **physico-chemical equations** describe ion association and dissociation, and gas-liquid transfer. \n",
+    "\n",
+    "Implemented as a differential and algebraic equation (DAE) set, there are 26 dynamic state concentration variables, and 8 implicit algebraic variables per reactor vessel or element. Implemented as differential equations (DE) only, there are 32 dynamic concentration state variables.\n",
+    "\n",
+    "*Water Science and Technology, Vol 45, No 10, pp 65–73*"
+   ]
+  },
+  {
+   "attachments": {
+    "ADM1.JPG": {
+     "image/jpeg": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "180af880",
+   "metadata": {},
+   "source": [
+    "![ADM1.JPG](attachment:ADM1.JPG)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "deab6410",
+   "metadata": {},
+   "source": [
+    "**Note:** You can find validation of the ADM1 system in [EXPOsan](https://github.com/QSD-Group/EXPOsan/tree/main/exposan/adm)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "47af6e27",
+   "metadata": {},
+   "source": [
+    "## 2. System Setup <a class=\"anchor\" id=\"s2\"></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "fb4e6486",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import packages\n",
+    "import numpy as np\n",
+    "from chemicals.elements import molecular_weight as get_mw\n",
+    "from qsdsan import sanunits as su, processes as pc, WasteStream, System\n",
+    "from qsdsan.utils import time_printer\n",
+    "\n",
+    "import warnings\n",
+    "warnings.simplefilter(action='ignore', category=FutureWarning)        # to ignore Pandas future warning"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8c7244dc",
+   "metadata": {},
+   "source": [
+    "### 2.1. State variables of ADM1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "5774fdae",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CompiledComponents([S_su, S_aa, S_fa, S_va, S_bu, S_pro, S_ac, S_h2, S_ch4, S_IC, S_IN, S_I, X_c, X_ch, X_pr, X_li, X_su, X_aa, X_fa, X_c4, X_pro, X_ac, X_h2, X_I, S_cat, S_an, H2O])\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Components \n",
+    "cmps = pc.create_adm1_cmps()           # create state variables for ADM1\n",
+    "cmps.show()                            # 26 components in ADM1 + water"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ee7c0b5",
+   "metadata": {},
+   "source": [
+    "**S_su**: Monosaccharides, **S_aa**: Amino acids, **S_fa**: Total long-chain fatty acids, **S_va**: Total valerate, **S_bu**: Total butyrate, **S_pro**: Total propionate, **S_ac**: Total acetate, **S_h2**: Hydrogen gas, **S_ch4**: Methane gas, **S_IC**: Inorganic carbon, **S_IN**: Inorganic nitrogen, **S_I**: Soluble inerts, **X_c**: Composites, **X_ch**: Carobohydrates, **X_pr**: Proteins, **X_li**: Lipids, **X_su**: Biomass uptaking sugars, **X_aa**: Biomass uptaking amino acids, **X_fa**: Biomass uptaking long chain fatty acids, **X_c4**: Biomass uptaking c4 fatty acids (valerate and butyrate), **X_pro**: Biomass uptaking propionate, **X_ac**: Biomass uptaking acetate, **X_h2**: Biomass uptaking hydrogen, **X_I**: Particulate inerts, **S_cat**: Other cations, **S_an**: Other anions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c4f28ea2",
+   "metadata": {},
+   "source": [
+    "### 2.2. The ADM1 `Process`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0dd6a5b8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ADM1([disintegration, hydrolysis_carbs, hydrolysis_proteins, hydrolysis_lipids, uptake_sugars, uptake_amino_acids, uptake_LCFA, uptake_valerate, uptake_butyrate, uptake_propionate, uptake_acetate, uptake_h2, decay_Xsu, decay_Xaa, decay_Xfa, decay_Xc4, decay_Xpro, decay_Xac, decay_Xh2, h2_transfer, ch4_transfer, IC_transfer])\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Processes\n",
+    "adm1 = pc.ADM1()                       # create ADM1 processes\n",
+    "adm1.show()                            # 22 processes in ADM1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "cc34c5f3",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'disintegration': <qsdsan._process.Process at 0x196b2859d30>,\n",
+       " 'hydrolysis_carbs': <qsdsan._process.Process at 0x196b2ad90a0>,\n",
+       " 'hydrolysis_proteins': <qsdsan._process.Process at 0x196b2859af0>,\n",
+       " 'hydrolysis_lipids': <qsdsan._process.Process at 0x196c46bce50>,\n",
+       " 'uptake_sugars': <qsdsan._process.Process at 0x196c46bce80>,\n",
+       " 'uptake_amino_acids': <qsdsan._process.Process at 0x196c46bceb0>,\n",
+       " 'uptake_LCFA': <qsdsan._process.Process at 0x196c4696f70>,\n",
+       " 'uptake_valerate': <qsdsan._process.Process at 0x196c4696c40>,\n",
+       " 'uptake_butyrate': <qsdsan._process.Process at 0x196c47c8730>,\n",
+       " 'uptake_propionate': <qsdsan._process.Process at 0x196c464e8b0>,\n",
+       " 'uptake_acetate': <qsdsan._process.Process at 0x196b2ad9490>,\n",
+       " 'uptake_h2': <qsdsan._process.Process at 0x196c4761040>,\n",
+       " 'decay_Xsu': <qsdsan._process.Process at 0x196c4796700>,\n",
+       " 'decay_Xaa': <qsdsan._process.Process at 0x196b2859a60>,\n",
+       " 'decay_Xfa': <qsdsan._process.Process at 0x196c46dda60>,\n",
+       " 'decay_Xc4': <qsdsan._process.Process at 0x196c47562b0>,\n",
+       " 'decay_Xpro': <qsdsan._process.Process at 0x196c4756070>,\n",
+       " 'decay_Xac': <qsdsan._process.Process at 0x196b2ad98e0>,\n",
+       " 'decay_Xh2': <qsdsan._process.Process at 0x196c464ef10>,\n",
+       " 'h2_transfer': <qsdsan._process.Process at 0x196c483e040>,\n",
+       " 'ch4_transfer': <qsdsan._process.Process at 0x196c465f370>,\n",
+       " 'IC_transfer': <qsdsan._process.Process at 0x196c4618550>,\n",
+       " 'tuple': (<qsdsan._process.Process at 0x196b2859d30>,\n",
+       "  <qsdsan._process.Process at 0x196b2ad90a0>,\n",
+       "  <qsdsan._process.Process at 0x196b2859af0>,\n",
+       "  <qsdsan._process.Process at 0x196c46bce50>,\n",
+       "  <qsdsan._process.Process at 0x196c46bce80>,\n",
+       "  <qsdsan._process.Process at 0x196c46bceb0>,\n",
+       "  <qsdsan._process.Process at 0x196c4696f70>,\n",
+       "  <qsdsan._process.Process at 0x196c4696c40>,\n",
+       "  <qsdsan._process.Process at 0x196c47c8730>,\n",
+       "  <qsdsan._process.Process at 0x196c464e8b0>,\n",
+       "  <qsdsan._process.Process at 0x196b2ad9490>,\n",
+       "  <qsdsan._process.Process at 0x196c4761040>,\n",
+       "  <qsdsan._process.Process at 0x196c4796700>,\n",
+       "  <qsdsan._process.Process at 0x196b2859a60>,\n",
+       "  <qsdsan._process.Process at 0x196c46dda60>,\n",
+       "  <qsdsan._process.Process at 0x196c47562b0>,\n",
+       "  <qsdsan._process.Process at 0x196c4756070>,\n",
+       "  <qsdsan._process.Process at 0x196b2ad98e0>,\n",
+       "  <qsdsan._process.Process at 0x196c464ef10>,\n",
+       "  <qsdsan._process.Process at 0x196c483e040>,\n",
+       "  <qsdsan._process.Process at 0x196c465f370>,\n",
+       "  <qsdsan._process.Process at 0x196c4618550>),\n",
+       " 'size': 22,\n",
+       " 'IDs': ('disintegration',\n",
+       "  'hydrolysis_carbs',\n",
+       "  'hydrolysis_proteins',\n",
+       "  'hydrolysis_lipids',\n",
+       "  'uptake_sugars',\n",
+       "  'uptake_amino_acids',\n",
+       "  'uptake_LCFA',\n",
+       "  'uptake_valerate',\n",
+       "  'uptake_butyrate',\n",
+       "  'uptake_propionate',\n",
+       "  'uptake_acetate',\n",
+       "  'uptake_h2',\n",
+       "  'decay_Xsu',\n",
+       "  'decay_Xaa',\n",
+       "  'decay_Xfa',\n",
+       "  'decay_Xc4',\n",
+       "  'decay_Xpro',\n",
+       "  'decay_Xac',\n",
+       "  'decay_Xh2',\n",
+       "  'h2_transfer',\n",
+       "  'ch4_transfer',\n",
+       "  'IC_transfer'),\n",
+       " '_index': {'disintegration': 0,\n",
+       "  'hydrolysis_carbs': 1,\n",
+       "  'hydrolysis_proteins': 2,\n",
+       "  'hydrolysis_lipids': 3,\n",
+       "  'uptake_sugars': 4,\n",
+       "  'uptake_amino_acids': 5,\n",
+       "  'uptake_LCFA': 6,\n",
+       "  'uptake_valerate': 7,\n",
+       "  'uptake_butyrate': 8,\n",
+       "  'uptake_propionate': 9,\n",
+       "  'uptake_acetate': 10,\n",
+       "  'uptake_h2': 11,\n",
+       "  'decay_Xsu': 12,\n",
+       "  'decay_Xaa': 13,\n",
+       "  'decay_Xfa': 14,\n",
+       "  'decay_Xc4': 15,\n",
+       "  'decay_Xpro': 16,\n",
+       "  'decay_Xac': 17,\n",
+       "  'decay_Xh2': 18,\n",
+       "  'h2_transfer': 19,\n",
+       "  'ch4_transfer': 20,\n",
+       "  'IC_transfer': 21},\n",
+       " '_components': CompiledComponents([S_su, S_aa, S_fa, S_va, S_bu, S_pro, S_ac, S_h2, S_ch4, S_IC, S_IN, S_I, X_c, X_ch, X_pr, X_li, X_su, X_aa, X_fa, X_c4, X_pro, X_ac, X_h2, X_I, S_cat, S_an, H2O]),\n",
+       " '_parameters': {'f_ch_xc': 0.2,\n",
+       "  'f_pr_xc': 0.2,\n",
+       "  'f_li_xc': 0.3,\n",
+       "  'f_xI_xc': 0.2,\n",
+       "  'f_sI_xc': 0.10000000000000009,\n",
+       "  'f_fa_li': 0.95,\n",
+       "  'f_bu_su': 0.13,\n",
+       "  'f_pro_su': 0.27,\n",
+       "  'f_ac_su': 0.41,\n",
+       "  'f_h2_su': 0.19,\n",
+       "  'f_va_aa': 0.23,\n",
+       "  'f_bu_aa': 0.26,\n",
+       "  'f_pro_aa': 0.05,\n",
+       "  'f_ac_aa': 0.4,\n",
+       "  'f_h2_aa': 0.06,\n",
+       "  'f_ac_fa': 0.7,\n",
+       "  'f_h2_fa': 0.30000000000000004,\n",
+       "  'f_pro_va': 0.54,\n",
+       "  'f_ac_va': 0.31,\n",
+       "  'f_h2_va': 0.14999999999999997,\n",
+       "  'f_ac_bu': 0.8,\n",
+       "  'f_h2_bu': 0.19999999999999996,\n",
+       "  'f_ac_pro': 0.57,\n",
+       "  'f_h2_pro': 0.43000000000000005,\n",
+       "  'Y_su': 0.1,\n",
+       "  'Y_aa': 0.08,\n",
+       "  'Y_fa': 0.06,\n",
+       "  'Y_c4': 0.06,\n",
+       "  'Y_pro': 0.04,\n",
+       "  'Y_ac': 0.05,\n",
+       "  'Y_h2': 0.06},\n",
+       " '_dyn_params': {},\n",
+       " '_stoichiometry': [[0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   -0.375348450566896*f_ch_xc - 0.264038220398782*f_li_xc - 0.360321*f_pr_xc - 0.360321*f_sI_xc - 0.360321*f_xI_xc + 0.334618102,\n",
+       "   -0.0980469*f_pr_xc - 0.0600327162*f_sI_xc - 0.0600327162*f_xI_xc + 0.0376219962,\n",
+       "   1.0*f_sI_xc,\n",
+       "   -1.00000000000000,\n",
+       "   1.0*f_ch_xc,\n",
+       "   1.0*f_pr_xc,\n",
+       "   1.0*f_li_xc,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   1.0*f_xI_xc,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0],\n",
+       "  [1.00000000000000,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   -5.55111512312578e-17,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   -1.00000000000000,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0],\n",
+       "  [0,\n",
+       "   1.00000000000000,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   -1.00000000000000,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0],\n",
+       "  [1.0 - 1.0*f_fa_li,\n",
+       "   0,\n",
+       "   1.0*f_fa_li,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0.114236484955142*f_fa_li - 0.111310230168114,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   -1.00000000000000,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0],\n",
+       "  [-1.00000000000000,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   1.0*f_bu_su*(1 - Y_su),\n",
+       "   1.0*f_pro_su*(1 - Y_su),\n",
+       "   1.0*f_ac_su*(1 - Y_su),\n",
+       "   1.0*f_h2_su*(1 - Y_su),\n",
+       "   0,\n",
+       "   0.375348450566896*Y_su*f_ac_su + 0.300278760453517*Y_su*f_bu_su + 0.321727243343054*Y_su*f_pro_su - 0.37593491*Y_su - 0.375348450566896*f_ac_su - 0.300278760453517*f_bu_su - 0.321727243343054*f_pro_su + 0.375348450566896,\n",
+       "   -0.08*Y_su,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   1.0*Y_su,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0],\n",
+       "  [0,\n",
+       "   -1.00000000000000,\n",
+       "   0,\n",
+       "   1.0*f_va_aa*(1 - Y_aa),\n",
+       "   1.0*f_bu_aa*(1 - Y_aa),\n",
+       "   1.0*f_pro_aa*(1 - Y_aa),\n",
+       "   1.0*f_ac_aa*(1 - Y_aa),\n",
+       "   1.0*f_h2_aa*(1 - Y_aa),\n",
+       "   0,\n",
+       "   0.375348450566896*Y_aa*f_ac_aa + 0.300278760453517*Y_aa*f_bu_aa + 0.321727243343054*Y_aa*f_pro_aa + 0.288729577359151*Y_aa*f_va_aa - 0.37593491*Y_aa - 0.375348450566896*f_ac_aa - 0.300278760453517*f_bu_aa - 0.321727243343054*f_pro_aa - 0.288729577359151*f_va_aa + 0.360321,\n",
+       "   0.0980469 - 0.08*Y_aa,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   1.0*Y_aa,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0],\n",
+       "  [0,\n",
+       "   0,\n",
+       "   -1.00000000000000,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   1.0*f_ac_fa*(1 - Y_fa),\n",
+       "   1.0*f_h2_fa*(1 - Y_fa),\n",
+       "   0,\n",
+       "   0.375348450566896*Y_fa*f_ac_fa - 0.37593491*Y_fa - 0.375348450566896*f_ac_fa + 0.261111965611754,\n",
+       "   -0.08*Y_fa,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   1.0*Y_fa,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0],\n",
+       "  [0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   -1.00000000000000,\n",
+       "   0,\n",
+       "   1.0*f_pro_va*(1 - Y_c4),\n",
+       "   1.0*f_ac_va*(1 - Y_c4),\n",
+       "   1.0*f_h2_va*(1 - Y_c4),\n",
+       "   0,\n",
+       "   0.375348450566896*Y_c4*f_ac_va + 0.321727243343054*Y_c4*f_pro_va - 0.37593491*Y_c4 - 0.375348450566896*f_ac_va - 0.321727243343054*f_pro_va + 0.288729577359151,\n",
+       "   -0.08*Y_c4,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   1.0*Y_c4,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0],\n",
+       "  [0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   -1.00000000000000,\n",
+       "   0,\n",
+       "   1.0*f_ac_bu*(1 - Y_c4),\n",
+       "   1.0*f_h2_bu*(1 - Y_c4),\n",
+       "   0,\n",
+       "   0.375348450566896*Y_c4*f_ac_bu - 0.37593491*Y_c4 - 0.375348450566896*f_ac_bu + 0.300278760453517,\n",
+       "   -0.08*Y_c4,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   1.0*Y_c4,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0],\n",
+       "  [0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   -1.00000000000000,\n",
+       "   1.0*f_ac_pro*(1 - Y_pro),\n",
+       "   1.0*f_h2_pro*(1 - Y_pro),\n",
+       "   0,\n",
+       "   0.375348450566896*Y_pro*f_ac_pro - 0.37593491*Y_pro - 0.375348450566896*f_ac_pro + 0.321727243343054,\n",
+       "   -0.08*Y_pro,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   1.0*Y_pro,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0],\n",
+       "  [0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   -1.00000000000000,\n",
+       "   0,\n",
+       "   1.0 - 1.0*Y_ac,\n",
+       "   0.187674225283448 - 0.188260684716552*Y_ac,\n",
+       "   -0.08*Y_ac,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   1.0*Y_ac,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0],\n",
+       "  [0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   -1.00000000000000,\n",
+       "   1.0 - 1.0*Y_h2,\n",
+       "   -0.188260684716552*Y_h2 - 0.187674225283448,\n",
+       "   -0.08*Y_h2,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
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+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   1.0*Y_h2,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0],\n",
+       "  [0,\n",
+       "   0,\n",
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+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0.0413168080000000,\n",
+       "   0.0423780038000000,\n",
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+       "   1.00000000000000,\n",
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+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0,\n",
+       "   0]],\n",
+       " '_stoichio_lambdified': None,\n",
+       " '_rate_equations': (None,\n",
+       "  None,\n",
+       "  None,\n",
+       "  None,\n",
+       "  None,\n",
+       "  None,\n",
+       "  None,\n",
+       "  None,\n",
+       "  None,\n",
+       "  None,\n",
+       "  None,\n",
+       "  None,\n",
+       "  None,\n",
+       "  None,\n",
+       "  None,\n",
+       "  None,\n",
+       "  None,\n",
+       "  None,\n",
+       "  None,\n",
+       "  None,\n",
+       "  None,\n",
+       "  None),\n",
+       " '_production_rates': None,\n",
+       " '_rate_function': <MultiKinetics(['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'])>}"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "adm1.__dict__                          # adm1 is composed of..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b3d103f",
+   "metadata": {},
+   "source": [
+    "### 2.3. Petersen matrix of ADM1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "9a9db08e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>S_su</th>\n",
+       "      <th>S_aa</th>\n",
+       "      <th>S_fa</th>\n",
+       "      <th>S_va</th>\n",
+       "      <th>S_bu</th>\n",
+       "      <th>...</th>\n",
+       "      <th>X_h2</th>\n",
+       "      <th>X_I</th>\n",
+       "      <th>S_cat</th>\n",
+       "      <th>S_an</th>\n",
+       "      <th>H2O</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>disintegration</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>0.2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>hydrolysis_carbs</th>\n",
+       "      <td>1</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>hydrolysis_proteins</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</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>hydrolysis_lipids</th>\n",
+       "      <td>0.05</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.95</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_sugars</th>\n",
+       "      <td>-1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.117</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_amino_acids</th>\n",
+       "      <td>0</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.212</td>\n",
+       "      <td>0.239</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_LCFA</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-1</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_valerate</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-1</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_butyrate</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-1</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_propionate</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_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": 6,
+     "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": "e2c2360d",
+   "metadata": {},
+   "source": [
+    "### 2.4. Influent & effluent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a28bc7d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Flow rate, temperature, HRT\n",
+    "Q = 170                                             # influent flowrate [m3/d]\n",
+    "Temp = 273.15+35                                    # temperature [K]\n",
+    "HRT = 5                                             # HRT [d]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "28a9c8e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# WasteStream\n",
+    "inf = WasteStream('Influent', T=Temp)               # influent\n",
+    "eff = WasteStream('Effluent', T=Temp)               # effluent\n",
+    "gas = WasteStream('Biogas')                         # gas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "bdd90569",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WasteStream: Influent\n",
+      "phase: 'l', T: 308.15 K, P: 101325 Pa\n",
+      "flow (g/hr): S_su   70.8\n",
+      "             S_aa   7.08\n",
+      "             S_fa   7.08\n",
+      "             S_va   7.08\n",
+      "             S_bu   7.08\n",
+      "             S_pro  7.08\n",
+      "             S_ac   7.08\n",
+      "             S_h2   7.08e-05\n",
+      "             S_ch4  0.0708\n",
+      "             S_IC   3.4e+03\n",
+      "             S_IN   992\n",
+      "             S_I    142\n",
+      "             X_c    1.42e+04\n",
+      "             X_ch   3.54e+04\n",
+      "             X_pr   1.42e+05\n",
+      "             ...    6.97e+06\n",
+      " WasteStream-specific properties:\n",
+      "  pH         : 7.0\n",
+      "  Alkalinity : 2.5 mg/L\n",
+      "  COD        : 57096.0 mg/L\n",
+      "  BOD        : 12769.4 mg/L\n",
+      "  TC         : 20596.5 mg/L\n",
+      "  TOC        : 20116.0 mg/L\n",
+      "  TN         : 3683.2 mg/L\n",
+      "  TP         : 489.3 mg/L\n",
+      "  TK         : 9.8 mg/L\n",
+      " Component concentrations (mg/L):\n",
+      "  S_su   10.0\n",
+      "  S_aa   1.0\n",
+      "  S_fa   1.0\n",
+      "  S_va   1.0\n",
+      "  S_bu   1.0\n",
+      "  S_pro  1.0\n",
+      "  S_ac   1.0\n",
+      "  S_h2   0.0\n",
+      "  S_ch4  0.0\n",
+      "  S_IC   480.4\n",
+      "  S_IN   140.1\n",
+      "  S_I    20.0\n",
+      "  X_c    2000.0\n",
+      "  X_ch   5000.0\n",
+      "  X_pr   20000.0\n",
+      "  ...\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Set influent concentration\n",
+    "C_mw = get_mw({'C':1})        # molecular weight of carbon\n",
+    "N_mw = get_mw({'N':1})        # molecular weight of nitrogen\n",
+    "\n",
+    "default_inf_kwargs = {\n",
+    "    'concentrations': {\n",
+    "        'S_su':0.01,\n",
+    "        'S_aa':1e-3,\n",
+    "        'S_fa':1e-3,\n",
+    "        'S_va':1e-3,\n",
+    "        'S_bu':1e-3,\n",
+    "        'S_pro':1e-3,\n",
+    "        'S_ac':1e-3,\n",
+    "        'S_h2':1e-8,\n",
+    "        'S_ch4':1e-5,\n",
+    "        'S_IC':0.04*C_mw,\n",
+    "        'S_IN':0.01*N_mw,\n",
+    "        'S_I':0.02,\n",
+    "        'X_c':2.0,\n",
+    "        'X_ch':5.0,\n",
+    "        'X_pr':20.0,\n",
+    "        'X_li':5.0,\n",
+    "        'X_aa':1e-2,\n",
+    "        'X_fa':1e-2,\n",
+    "        'X_c4':1e-2,\n",
+    "        'X_pro':1e-2,\n",
+    "        'X_ac':1e-2,\n",
+    "        'X_h2':1e-2,\n",
+    "        'X_I':25,\n",
+    "        'S_cat':0.04,\n",
+    "        'S_an':0.02,\n",
+    "        },\n",
+    "    'units': ('m3/d', 'kg/m3'),\n",
+    "    }                                                           # concentration of each state variable in influent\n",
+    "\n",
+    "inf.set_flow_by_concentration(Q, **default_inf_kwargs)          # set influent concentration\n",
+    "inf"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4bf9c287",
+   "metadata": {},
+   "source": [
+    "### 2.5. Reactor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "1fc90df0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# SanUnit\n",
+    "AD = su.AnaerobicCSTR('AD', ins=inf, outs=(gas, eff), model=adm1, V_liq=Q*HRT, V_gas=Q*HRT*0.1, T=Temp)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0716d4c9",
+   "metadata": {},
+   "source": [
+    "**su.AnaerobicCSTR**(\n",
+    "    ID='',\n",
+    "    ins=None,\n",
+    "    outs=(),\n",
+    "    thermo=None,\n",
+    "    init_with='WasteStream',\n",
+    "    V_liq=3400,\n",
+    "    V_gas=300,\n",
+    "    model=None,\n",
+    "    T=308.15,\n",
+    "    headspace_P=1.013,\n",
+    "    external_P=1.013,\n",
+    "    pipe_resistance=50000.0,\n",
+    "    fixed_headspace_P=False,\n",
+    "    retain_cmps=(),\n",
+    "    fraction_retain=0.95,\n",
+    "    isdynamic=True,\n",
+    "    exogenous_vars=(),\n",
+    "    **kwargs,\n",
+    ")\n",
+    "\n",
+    "**Parameters**<br>\n",
+    "*ins* : :class:`WasteStream`,\n",
+    "    Influent to the reactor.<br>\n",
+    "*outs* : Iterable,\n",
+    "    Biogas and treated effluent(s).<br>\n",
+    "*V_liq* : float, optional,\n",
+    "    Liquid-phase volume [m^3]. The default is 3400.<br>\n",
+    "*V_gas* : float, optional,\n",
+    "    Headspace volume [m^3]. The default is 300.<br>\n",
+    "*model* : :class:`Processes`, optional,\n",
+    "    The kinetic model, typically ADM1-like. The default is None.<br>\n",
+    "*T* : float, optional,\n",
+    "    Operation temperature [K]. The default is 308.15.<br>\n",
+    "*headspace_P* : float, optional,\n",
+    "    Headspace pressure, if fixed [bar]. The default is 1.013.<br>\n",
+    "*external_P* : float, optional,\n",
+    "    External pressure, typically atmospheric pressure [bar]. The default is 1.013.<br>\n",
+    "*pipe_resistance* : float, optional,\n",
+    "    Biogas extraction pipe resistance [m3/d/bar]. The default is 5.0e4.<br>\n",
+    "*fixed_headspace_P* : bool, optional,\n",
+    "    Whether to assume fixed headspace pressure. The default is False.<br>\n",
+    "*retain_cmps* : Iterable[str], optional,\n",
+    "    IDs of the components that are assumed to be retained in the reactor, ideally.\n",
+    "    The default is ().<br>\n",
+    "*fraction_retain* : float, optional,\n",
+    "    The assumed fraction of ideal retention of select components. The default is 0.95.<br>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "4d403072",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"291pt\" height=\"85pt\" viewBox=\"0.00 0.00 291.00 85.00\">\n",
+       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 81)\">\n",
+       "<!-- AD\n",
+       "Anaerobic CSTR&#45;&gt;109170427693 -->\n",
+       "<g id=\"edge2\" class=\"edge\">\n",
+       "<title>AD\n",
+       "Anaerobic CSTR:c-&gt;109170427693:w</title>\n",
+       "<path fill=\"none\" stroke=\"#90918e\" stroke-dasharray=\"5,2\" d=\"M203.04,-53.6C225.06,-58.77 250.19,-63 273.5,-63\"/>\n",
+       "<g id=\"a_edge2-label\"><a xlink:title=\"(empty)\">\n",
+       "<text text-anchor=\"middle\" x=\"237.5\" y=\"-65.8\" font-family=\"Arial\" font-size=\"14.00\" fill=\"#90918e\"> Biogas</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- AD\n",
+       "Anaerobic CSTR&#45;&gt;109170428053 -->\n",
+       "<g id=\"edge3\" class=\"edge\">\n",
+       "<title>AD\n",
+       "Anaerobic CSTR:c-&gt;109170428053:w</title>\n",
+       "<path fill=\"none\" stroke=\"#90918e\" stroke-dasharray=\"5,2\" d=\"M203.04,-16.4C225.06,-11.23 250.19,-7 273.5,-7\"/>\n",
+       "<g id=\"a_edge3-label\"><a xlink:title=\"(empty)\">\n",
+       "<text text-anchor=\"middle\" x=\"237.5\" y=\"-18.8\" font-family=\"Arial\" font-size=\"14.00\" fill=\"#90918e\"> Effluent</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- 109170427413&#45;&gt;AD\n",
+       "Anaerobic CSTR -->\n",
+       "<g id=\"edge1\" class=\"edge\">\n",
+       "<title>109170427413:e-&gt;AD\n",
+       "Anaerobic CSTR:c</title>\n",
+       "<path fill=\"none\" stroke=\"#90918e\" d=\"M11,-35C32.75,-35 56.57,-35 77.73,-35\"/>\n",
+       "<g id=\"a_edge1-label\"><a xlink:title=\"Temperature: 308.15 K\n",
+       "Pressure: 101325 Pa\n",
+       "Liquid flow: 709 kmol/hr\">\n",
+       "<text text-anchor=\"middle\" x=\"44.5\" y=\"-38.8\" font-family=\"Arial\" font-size=\"14.00\" fill=\"#90918e\"> Influent</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- AD\n",
+       "Anaerobic CSTR -->\n",
+       "<g id=\"node1\" class=\"node\">\n",
+       "<title>AD\n",
+       "Anaerobic CSTR</title>\n",
+       "<g id=\"a_node1\"><a xlink:title=\"No capital costs or utilities\">\n",
+       "<polygon fill=\"#555f69\" stroke=\"#90918e\" points=\"203,-56.5 78,-56.5 78,-13.5 203,-13.5 203,-56.5\"/>\n",
+       "<text text-anchor=\"middle\" x=\"140.5\" y=\"-38.8\" font-family=\"Arial\" font-size=\"14.00\" fill=\"white\">AD</text>\n",
+       "<text text-anchor=\"middle\" x=\"140.5\" y=\"-23.8\" font-family=\"Arial\" font-size=\"14.00\" fill=\"white\">Anaerobic CSTR</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- 109170427413 -->\n",
+       "<g id=\"node2\" class=\"node\">\n",
+       "<title>109170427413</title>\n",
+       "<polygon fill=\"#f98f60\" stroke=\"#90918e\" points=\"5.5,-40.5 0,-35 5.5,-29.5 11,-35 5.5,-40.5\"/>\n",
+       "</g>\n",
+       "<!-- 109170427693 -->\n",
+       "<g id=\"node3\" class=\"node\">\n",
+       "<title>109170427693</title>\n",
+       "<polygon fill=\"#f98f60\" stroke=\"#90918e\" points=\"283,-63 274.75,-70 274.75,-56 283,-63\"/>\n",
+       "</g>\n",
+       "<!-- 109170428053 -->\n",
+       "<g id=\"node4\" class=\"node\">\n",
+       "<title>109170428053</title>\n",
+       "<polygon fill=\"#f98f60\" stroke=\"#90918e\" points=\"283,-7 274.75,-14 274.75,0 283,-7\"/>\n",
+       "</g>\n",
+       "</g>\n",
+       "</svg>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.SVG object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AnaerobicCSTR: AD\n",
+      "ins...\n",
+      "[0] Influent\n",
+      "phase: 'l', T: 308.15 K, P: 101325 Pa\n",
+      "flow (g/hr): S_su   70.8\n",
+      "                S_aa   7.08\n",
+      "                S_fa   7.08\n",
+      "                S_va   7.08\n",
+      "                S_bu   7.08\n",
+      "                S_pro  7.08\n",
+      "                S_ac   7.08\n",
+      "                S_h2   7.08e-05\n",
+      "                S_ch4  0.0708\n",
+      "                S_IC   3.4e+03\n",
+      "                S_IN   992\n",
+      "                S_I    142\n",
+      "                X_c    1.42e+04\n",
+      "                X_ch   3.54e+04\n",
+      "                X_pr   1.42e+05\n",
+      "                ...    6.97e+06\n",
+      "    WasteStream-specific properties:\n",
+      "     pH         : 7.0\n",
+      "     COD        : 57096.0 mg/L\n",
+      "     BOD        : 12769.4 mg/L\n",
+      "     TC         : 20596.5 mg/L\n",
+      "     TOC        : 20116.0 mg/L\n",
+      "     TN         : 3683.2 mg/L\n",
+      "     TP         : 489.3 mg/L\n",
+      "     TK         : 9.8 mg/L\n",
+      "outs...\n",
+      "[0] Biogas\n",
+      "phase: 'l', T: 298.15 K, P: 101325 Pa\n",
+      "flow: 0\n",
+      "    WasteStream-specific properties: None for empty waste streams\n",
+      "[1] Effluent\n",
+      "phase: 'l', T: 308.15 K, P: 101325 Pa\n",
+      "flow: 0\n",
+      "    WasteStream-specific properties: None for empty waste streams\n"
+     ]
+    }
+   ],
+   "source": [
+    "AD                                                             # anaerobic CSTR with influent, effluent, and biogas\n",
+    "                                                               # before running the simulation, 'outs' have nothing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "b162ac79",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set initial condition of the reactor\n",
+    "default_init_conds = {\n",
+    "    'S_su': 0.0124*1e3,\n",
+    "    'S_aa': 0.0055*1e3,\n",
+    "    'S_fa': 0.1074*1e3,\n",
+    "    'S_va': 0.0123*1e3,\n",
+    "    'S_bu': 0.0140*1e3,\n",
+    "    'S_pro': 0.0176*1e3,\n",
+    "    'S_ac': 0.0893*1e3,\n",
+    "    'S_h2': 2.5055e-7*1e3,\n",
+    "    'S_ch4': 0.0555*1e3,\n",
+    "    'S_IC': 0.0951*C_mw*1e3,\n",
+    "    'S_IN': 0.0945*N_mw*1e3,\n",
+    "    'S_I': 0.1309*1e3,\n",
+    "    'X_ch': 0.0205*1e3,\n",
+    "    'X_pr': 0.0842*1e3,\n",
+    "    'X_li': 0.0436*1e3,\n",
+    "    'X_su': 0.3122*1e3,\n",
+    "    'X_aa': 0.9317*1e3,\n",
+    "    'X_fa': 0.3384*1e3,\n",
+    "    'X_c4': 0.3258*1e3,\n",
+    "    'X_pro': 0.1011*1e3,\n",
+    "    'X_ac': 0.6772*1e3,\n",
+    "    'X_h2': 0.2848*1e3,\n",
+    "    'X_I': 17.2162*1e3\n",
+    "    }                                                           # concentration of each state variable in reactor\n",
+    "\n",
+    "AD.set_init_conc(**default_init_conds)                          # set initial condition of AD"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "051f6b47",
+   "metadata": {},
+   "source": [
+    "### 2.6. System set-up"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "85b13876",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"307pt\" height=\"51pt\" viewBox=\"0.00 0.00 307.00 51.00\">\n",
+       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 47)\">\n",
+       "<!-- Influent&#45;&gt;Anaerobic_Digestion\n",
+       "System -->\n",
+       "<g id=\"edge1\" class=\"edge\">\n",
+       "<title>Influent:c-&gt;Anaerobic_Digestion\n",
+       "System:c</title>\n",
+       "<path fill=\"none\" stroke=\"#90918e\" d=\"M63.02,-21.5C63.23,-21.5 63.45,-21.5 63.67,-21.5\"/>\n",
+       "<polygon fill=\"#90918e\" stroke=\"#90918e\" points=\"63.83,-25 73.83,-21.5 63.83,-18 63.83,-25\"/>\n",
+       "</g>\n",
+       "<!-- Anaerobic_Digestion\n",
+       "System&#45;&gt;Biogas\n",
+       "Effluent -->\n",
+       "<g id=\"edge2\" class=\"edge\">\n",
+       "<title>Anaerobic_Digestion\n",
+       "System:c-&gt;Biogas\n",
+       "Effluent:c</title>\n",
+       "<path fill=\"none\" stroke=\"#90918e\" stroke-dasharray=\"5,2\" d=\"M223.29,-21.5C223.4,-21.5 223.52,-21.5 223.63,-21.5\"/>\n",
+       "<polygon fill=\"#90918e\" stroke=\"#90918e\" points=\"223.7,-25 233.7,-21.5 223.7,-18 223.7,-25\"/>\n",
+       "</g>\n",
+       "<!-- Influent -->\n",
+       "<g id=\"node1\" class=\"node\">\n",
+       "<title>Influent</title>\n",
+       "<g id=\"a_node1\"><a xlink:title=\" \">\n",
+       "<polygon fill=\"#60c1cf\" stroke=\"#55a8b5\" points=\"63,-43 0,-43 0,0 63,0 63,-43\"/>\n",
+       "<text text-anchor=\"middle\" x=\"31.5\" y=\"-17.8\" font-family=\"Arial\" font-size=\"14.00\" fill=\"white\">Influent</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- Anaerobic_Digestion\n",
+       "System -->\n",
+       "<g id=\"node2\" class=\"node\">\n",
+       "<title>Anaerobic_Digestion\n",
+       "System</title>\n",
+       "<g id=\"a_node2\"><a xlink:title=\" \">\n",
+       "<polygon fill=\"#555f69\" stroke=\"#90918e\" points=\"223,-43 74,-43 74,0 223,0 223,-43\"/>\n",
+       "<text text-anchor=\"middle\" x=\"148.5\" y=\"-25.3\" font-family=\"Arial\" font-size=\"14.00\" fill=\"white\">Anaerobic_Digestion</text>\n",
+       "<text text-anchor=\"middle\" x=\"148.5\" y=\"-10.3\" font-family=\"Arial\" font-size=\"14.00\" fill=\"white\">System</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- Biogas\n",
+       "Effluent -->\n",
+       "<g id=\"node3\" class=\"node\">\n",
+       "<title>Biogas\n",
+       "Effluent</title>\n",
+       "<g id=\"a_node3\"><a xlink:title=\" \">\n",
+       "<polygon fill=\"#60c1cf\" stroke=\"#55a8b5\" points=\"299,-43 234,-43 234,0 299,0 299,-43\"/>\n",
+       "<text text-anchor=\"middle\" x=\"266.5\" y=\"-25.3\" font-family=\"Arial\" font-size=\"14.00\" fill=\"white\">Biogas</text>\n",
+       "<text text-anchor=\"middle\" x=\"266.5\" y=\"-10.3\" font-family=\"Arial\" font-size=\"14.00\" fill=\"white\">Effluent</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "</g>\n",
+       "</svg>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.SVG object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "System: Anaerobic_Digestion\n",
+      "ins...\n",
+      "[0] Influent  \n",
+      "    phase: 'l', T: 308.15 K, P: 101325 Pa\n",
+      "    flow (kmol/hr): S_su   0.000393\n",
+      "                    S_aa   0.00708\n",
+      "                    S_fa   2.76e-05\n",
+      "                    S_va   6.94e-05\n",
+      "                    S_bu   8.13e-05\n",
+      "                    S_pro  9.69e-05\n",
+      "                    S_ac   0.00012\n",
+      "                    ...    709\n",
+      "outs...\n",
+      "[0] Biogas  \n",
+      "    phase: 'l', T: 298.15 K, P: 101325 Pa\n",
+      "    flow: 0\n",
+      "[1] Effluent  \n",
+      "    phase: 'l', T: 308.15 K, P: 101325 Pa\n",
+      "    flow: 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# System\n",
+    "sys = System('Anaerobic_Digestion', path=(AD,))                 # aggregation of sanunits\n",
+    "sys.set_dynamic_tracker(eff, gas)                               # what you want to track changes in concentration\n",
+    "sys                                                             # before running the simulation, 'outs' have nothing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd50264c",
+   "metadata": {},
+   "source": [
+    "## 3. System Simulation <a class=\"anchor\" id=\"s3\"></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "132152fe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Simulation settings\n",
+    "t = 10                          # total time for simulation\n",
+    "t_step = 0.1                    # times at which to store the computed solution              \n",
+    "\n",
+    "method = 'BDF'                  # integration method to use\n",
+    "# method = 'RK45'\n",
+    "# method = 'RK23'\n",
+    "# method = 'DOP853'\n",
+    "# method = 'Radau'\n",
+    "# method = 'LSODA'\n",
+    "\n",
+    "# https://docs.scipy.org/doc/scipy/reference/generated/scipy.integrate.solve_ivp.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "74bcbaf0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Run simulation\n",
+    "sys.simulate(state_reset_hook='reset_cache',\n",
+    "             t_span=(0,t),\n",
+    "             t_eval=np.arange(0, t+t_step, t_step),\n",
+    "             method=method,\n",
+    "            # export_state_to=f'sol_{t}d_{method}_AD.xlsx',               # uncomment to export simulation result as excel file\n",
+    "            )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "55247c4c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"307pt\" height=\"51pt\" viewBox=\"0.00 0.00 307.00 51.00\">\n",
+       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 47)\">\n",
+       "<!-- Influent&#45;&gt;Anaerobic_Digestion\n",
+       "System -->\n",
+       "<g id=\"edge1\" class=\"edge\">\n",
+       "<title>Influent:c-&gt;Anaerobic_Digestion\n",
+       "System:c</title>\n",
+       "<path fill=\"none\" stroke=\"#90918e\" d=\"M63.02,-21.5C63.23,-21.5 63.45,-21.5 63.67,-21.5\"/>\n",
+       "<polygon fill=\"#90918e\" stroke=\"#90918e\" points=\"63.83,-25 73.83,-21.5 63.83,-18 63.83,-25\"/>\n",
+       "</g>\n",
+       "<!-- Anaerobic_Digestion\n",
+       "System&#45;&gt;Effluent\n",
+       "Biogas -->\n",
+       "<g id=\"edge2\" class=\"edge\">\n",
+       "<title>Anaerobic_Digestion\n",
+       "System:c-&gt;Effluent\n",
+       "Biogas:c</title>\n",
+       "<path fill=\"none\" stroke=\"#90918e\" stroke-dasharray=\"5,2\" d=\"M223.29,-21.5C223.4,-21.5 223.52,-21.5 223.63,-21.5\"/>\n",
+       "<polygon fill=\"#90918e\" stroke=\"#90918e\" points=\"223.7,-25 233.7,-21.5 223.7,-18 223.7,-25\"/>\n",
+       "</g>\n",
+       "<!-- Influent -->\n",
+       "<g id=\"node1\" class=\"node\">\n",
+       "<title>Influent</title>\n",
+       "<g id=\"a_node1\"><a xlink:title=\" \">\n",
+       "<polygon fill=\"#60c1cf\" stroke=\"#55a8b5\" points=\"63,-43 0,-43 0,0 63,0 63,-43\"/>\n",
+       "<text text-anchor=\"middle\" x=\"31.5\" y=\"-17.8\" font-family=\"Arial\" font-size=\"14.00\" fill=\"white\">Influent</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- Anaerobic_Digestion\n",
+       "System -->\n",
+       "<g id=\"node2\" class=\"node\">\n",
+       "<title>Anaerobic_Digestion\n",
+       "System</title>\n",
+       "<g id=\"a_node2\"><a xlink:title=\" \">\n",
+       "<polygon fill=\"#555f69\" stroke=\"#90918e\" points=\"223,-43 74,-43 74,0 223,0 223,-43\"/>\n",
+       "<text text-anchor=\"middle\" x=\"148.5\" y=\"-25.3\" font-family=\"Arial\" font-size=\"14.00\" fill=\"white\">Anaerobic_Digestion</text>\n",
+       "<text text-anchor=\"middle\" x=\"148.5\" y=\"-10.3\" font-family=\"Arial\" font-size=\"14.00\" fill=\"white\">System</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- Effluent\n",
+       "Biogas -->\n",
+       "<g id=\"node3\" class=\"node\">\n",
+       "<title>Effluent\n",
+       "Biogas</title>\n",
+       "<g id=\"a_node3\"><a xlink:title=\" \">\n",
+       "<polygon fill=\"#60c1cf\" stroke=\"#55a8b5\" points=\"299,-43 234,-43 234,0 299,0 299,-43\"/>\n",
+       "<text text-anchor=\"middle\" x=\"266.5\" y=\"-25.3\" font-family=\"Arial\" font-size=\"14.00\" fill=\"white\">Effluent</text>\n",
+       "<text text-anchor=\"middle\" x=\"266.5\" y=\"-10.3\" font-family=\"Arial\" font-size=\"14.00\" fill=\"white\">Biogas</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "</g>\n",
+       "</svg>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.SVG object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "System: Anaerobic_Digestion\n",
+      "ins...\n",
+      "[0] Influent  \n",
+      "    phase: 'l', T: 308.15 K, P: 101325 Pa\n",
+      "    flow (kmol/hr): S_su   0.000393\n",
+      "                    S_aa   0.00708\n",
+      "                    S_fa   2.76e-05\n",
+      "                    S_va   6.94e-05\n",
+      "                    S_bu   8.13e-05\n",
+      "                    S_pro  9.69e-05\n",
+      "                    S_ac   0.00012\n",
+      "                    ...    709\n",
+      "outs...\n",
+      "[0] Biogas  \n",
+      "    phase: 'g', T: 308.15 K, P: 101325 Pa\n",
+      "    flow (kmol/hr): S_h2   0.00119\n",
+      "                    S_ch4  8.5\n",
+      "                    S_IC   0.414\n",
+      "                    H2O    0.205\n",
+      "[1] Effluent  \n",
+      "    phase: 'l', T: 308.15 K, P: 101325 Pa\n",
+      "    flow (kmol/hr): S_su   0.00164\n",
+      "                    S_aa   0.129\n",
+      "                    S_fa   0.0222\n",
+      "                    S_va   0.00332\n",
+      "                    S_bu   0.00447\n",
+      "                    S_pro  0.0106\n",
+      "                    S_ac   0.639\n",
+      "                    ...    587\n"
+     ]
+    }
+   ],
+   "source": [
+    "sys                                                                      # now you have 'outs' info."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7b57f738",
+   "metadata": {},
+   "source": [
+    "### 3.1. Check simulation results: Effluent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "990d5e59",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(<Figure size 800x450 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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\n",
+      "text/plain": [
+       "<Figure size 800x450 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "eff.scope.plot_time_series(('S_aa', 'S_fa', 'S_va', 'S_bu', 'S_pro', 'S_ac'))  # you can plot how each state variable changes over time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "6f674fab",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(<Figure size 800x450 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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\n",
+      "text/plain": [
+       "<Figure size 800x450 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "eff.scope.plot_time_series(('S_IC'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "2ea79de8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(<Figure size 800x450 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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\n",
+      "text/plain": [
+       "<Figure size 800x450 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. Check simulation results: Gas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "d54aeb58",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(<Figure size 800x450 with 1 Axes>,\n",
+       " <Axes: xlabel='Time [d]', ylabel='Concentration [mg/L]'>)"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 800x450 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "gas.scope.plot_time_series(('S_h2'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "e021d8fb",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(<Figure size 800x450 with 1 Axes>,\n",
+       " <Axes: xlabel='Time [d]', ylabel='Concentration [mg/L]'>)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 800x450 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "gas.scope.plot_time_series(('S_ch4','S_IC'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ccde4d80",
+   "metadata": {},
+   "source": [
+    "### 3.3. Check simulation results: Total VFAs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "56f3fad7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Total VFAs = 'S_va' + 'S_bu' + 'S_pro' + 'S_ac'    (you can change the equations based on your assumption)\n",
+    "idx_vfa = cmps.indices(['S_va', 'S_bu', 'S_pro', 'S_ac'])\n",
+    "\n",
+    "t_stamp = eff.scope.time_series\n",
+    "\n",
+    "vfa = eff.scope.record[:,idx_vfa]\n",
+    "total_vfa = np.sum(vfa, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "a879f514",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Total VFA [mg/l]')"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "plt.plot(t_stamp, total_vfa)\n",
+    "plt.xlabel(\"Time [day]\")\n",
+    "plt.ylabel(\"Total VFA [mg/l]\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/source/tutorials/1_Helpful_Basics.ipynb b/docs/source/tutorials/1_Helpful_Basics.ipynb
index 81bc154b..6f5abeb0 100644
--- a/docs/source/tutorials/1_Helpful_Basics.ipynb
+++ b/docs/source/tutorials/1_Helpful_Basics.ipynb
@@ -9,8 +9,8 @@
     "\n",
     "- **Prepared by:**\n",
     "    \n",
-    "    - [Yalin Li](https://qsdsan.readthedocs.io/en/latest/authors/Yalin_Li.html)\n",
-    "    - [Joy Zhang](https://qsdsan.readthedocs.io/en/latest/authors/Joy_Zhang.html)\n",
+    "    - [Yalin Li](https://qsdsan.readthedocs.io/en/latest/CONTRIBUTING.html)\n",
+    "    - [Joy Zhang](https://qsdsan.readthedocs.io/en/latest/CONTRIBUTING.html)\n",
     "    \n",
     "- **Covered topics:**\n",
     "\n",
@@ -19,7 +19,7 @@
     "    \n",
     "- **Video demo:**\n",
     "\n",
-    "    - [Yalin Li](https://qsdsan.readthedocs.io/en/latest/authors/Yalin_Li.html)\n",
+    "    - [Yalin Li](https://qsdsan.readthedocs.io/en/latest/CONTRIBUTING.html)\n",
     "    \n",
     "To run tutorials in your browser, go to this [Binder page](https://mybinder.org/v2/gh/QSD-Group/QSDsan/main?filepath=%2Fdocs%2Fsource%2Ftutorials).\n",
     "    \n",
@@ -690,7 +690,36 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.13"
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
   }
  },
  "nbformat": 4,
diff --git a/docs/source/tutorials/2_Component.ipynb b/docs/source/tutorials/2_Component.ipynb
index 3e667b23..54c80ae2 100644
--- a/docs/source/tutorials/2_Component.ipynb
+++ b/docs/source/tutorials/2_Component.ipynb
@@ -8,8 +8,8 @@
     "\n",
     "- **Prepared by:**\n",
     "    \n",
-    "    - [Yalin Li](https://qsdsan.readthedocs.io/en/latest/authors/Yalin_Li.html)\n",
-    "    - [Joy Zhang](https://qsdsan.readthedocs.io/en/latest/authors/Joy_Zhang.html)\n",
+    "    - [Yalin Li](https://qsdsan.readthedocs.io/en/latest/CONTRIBUTING.html)\n",
+    "    - [Joy Zhang](https://qsdsan.readthedocs.io/en/latest/CONTRIBUTING.html)\n",
     "\n",
     "- **Covered topics:**\n",
     "\n",
@@ -1575,6 +1575,35 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.9.13"
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
   }
  },
  "nbformat": 4,
diff --git a/docs/source/tutorials/3_WasteStream.ipynb b/docs/source/tutorials/3_WasteStream.ipynb
index 6fb337ee..6b37883e 100644
--- a/docs/source/tutorials/3_WasteStream.ipynb
+++ b/docs/source/tutorials/3_WasteStream.ipynb
@@ -8,8 +8,8 @@
     "\n",
     "- **Prepared by:**\n",
     "    \n",
-    "    - [Yalin Li](https://qsdsan.readthedocs.io/en/latest/authors/Yalin_Li.html)\n",
-    "    - [Joy Zhang](https://qsdsan.readthedocs.io/en/latest/authors/Joy_Zhang.html)\n",
+    "    - [Yalin Li](https://qsdsan.readthedocs.io/en/latest/CONTRIBUTING.html)\n",
+    "    - [Joy Zhang](https://qsdsan.readthedocs.io/en/latest/CONTRIBUTING.html)\n",
     "\n",
     "- **Covered topics:**\n",
     "\n",
@@ -1012,7 +1012,36 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.13"
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
   }
  },
  "nbformat": 4,
diff --git a/docs/source/tutorials/4_SanUnit_basic.ipynb b/docs/source/tutorials/4_SanUnit_basic.ipynb
index 6eaf6216..54eb061e 100644
--- a/docs/source/tutorials/4_SanUnit_basic.ipynb
+++ b/docs/source/tutorials/4_SanUnit_basic.ipynb
@@ -8,7 +8,7 @@
     "\n",
     "- **Prepared by:**\n",
     "    \n",
-    "    - [Yalin Li](https://qsdsan.readthedocs.io/en/latest/authors/Yalin_Li.html)\n",
+    "    - [Yalin Li](https://qsdsan.readthedocs.io/en/latest/CONTRIBUTING.html)\n",
     "\n",
     "- **Covered topics:**\n",
     "\n",
@@ -17,7 +17,7 @@
     "    \n",
     "- **Video demo:**\n",
     "\n",
-    "    - [Tori Morgan](https://qsdsan.readthedocs.io/en/latest/authors/Tori_Morgan.html)\n",
+    "    - [Tori Morgan](https://qsdsan.readthedocs.io/en/latest/CONTRIBUTING.html)\n",
     "    \n",
     "To run tutorials in your browser, go to this [Binder page](https://mybinder.org/v2/gh/QSD-Group/QSDsan/main?filepath=%2Fdocs%2Fsource%2Ftutorials).\n",
     "\n",
@@ -1113,6 +1113,35 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.9.13"
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
   }
  },
  "nbformat": 4,
diff --git a/docs/source/tutorials/5_SanUnit_advanced.ipynb b/docs/source/tutorials/5_SanUnit_advanced.ipynb
index 4ba7fdf3..e97d0a3a 100644
--- a/docs/source/tutorials/5_SanUnit_advanced.ipynb
+++ b/docs/source/tutorials/5_SanUnit_advanced.ipynb
@@ -8,7 +8,7 @@
     "\n",
     "- **Prepared by:**\n",
     "    \n",
-    "    - [Yalin Li](https://qsdsan.readthedocs.io/en/latest/authors/Yalin_Li.html)\n",
+    "    - [Yalin Li](https://qsdsan.readthedocs.io/en/latest/CONTRIBUTING.html)\n",
     "\n",
     "- **Covered topics:**\n",
     "\n",
@@ -19,7 +19,7 @@
     "    \n",
     "- **Video demo:**\n",
     "\n",
-    "    - [Hannah Lohman](https://qsdsan.readthedocs.io/en/latest/authors/Hannah_Lohman.html)\n",
+    "    - [Hannah Lohman](https://qsdsan.readthedocs.io/en/latest/CONTRIBUTING.html)\n",
     "    \n",
     "To run tutorials in your browser, go to this [Binder page](https://mybinder.org/v2/gh/QSD-Group/QSDsan/main?filepath=%2Fdocs%2Fsource%2Ftutorials).\n",
     "\n",
@@ -1614,6 +1614,35 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.9.13"
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
   }
  },
  "nbformat": 4,
diff --git a/docs/source/tutorials/6_System.ipynb b/docs/source/tutorials/6_System.ipynb
index 875fb145..1722504b 100644
--- a/docs/source/tutorials/6_System.ipynb
+++ b/docs/source/tutorials/6_System.ipynb
@@ -8,7 +8,7 @@
     "\n",
     "* **Prepared by:**\n",
     "    \n",
-    "    - [Yalin Li](https://qsdsan.readthedocs.io/en/latest/authors/Yalin_Li.html)\n",
+    "    - [Yalin Li](https://qsdsan.readthedocs.io/en/latest/CONTRIBUTING.html)\n",
     "\n",
     "* **Covered topics:**\n",
     "\n",
@@ -17,7 +17,7 @@
     "\n",
     "- **Video demo:**\n",
     "\n",
-    "    - [Tori Morgan](https://qsdsan.readthedocs.io/en/latest/authors/Tori_Morgan.html)\n",
+    "    - [Tori Morgan](https://qsdsan.readthedocs.io/en/latest/CONTRIBUTING.html)\n",
     "\n",
     "To run tutorials in your browser, go to this [Binder page](https://mybinder.org/v2/gh/QSD-Group/QSDsan/main?filepath=%2Fdocs%2Fsource%2Ftutorials).\n",
     "\n",
@@ -668,7 +668,36 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.13"
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
   }
  },
  "nbformat": 4,
diff --git a/docs/source/tutorials/7_TEA.ipynb b/docs/source/tutorials/7_TEA.ipynb
index 559b9b74..184d10c5 100755
--- a/docs/source/tutorials/7_TEA.ipynb
+++ b/docs/source/tutorials/7_TEA.ipynb
@@ -8,7 +8,7 @@
     "\n",
     "* **Prepared by:**\n",
     "    \n",
-    "    - [Yalin Li](https://qsdsan.readthedocs.io/en/latest/authors/Yalin_Li.html)\n",
+    "    - [Yalin Li](https://qsdsan.readthedocs.io/en/latest/CONTRIBUTING.html)\n",
     "\n",
     "* **Covered topics:**\n",
     "\n",
@@ -17,7 +17,7 @@
     "    \n",
     "- **Video demo:**\n",
     "\n",
-    "    - [Hannah Lohman](https://qsdsan.readthedocs.io/en/latest/authors/Hannah_Lohman.html)\n",
+    "    - [Hannah Lohman](https://qsdsan.readthedocs.io/en/latest/CONTRIBUTING.html)\n",
     "\n",
     "To run tutorials in your browser, go to this [Binder page](https://mybinder.org/v2/gh/QSD-Group/QSDsan/main?filepath=%2Fdocs%2Fsource%2Ftutorials).\n",
     "\n",
@@ -729,6 +729,35 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.9.13"
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
   }
  },
  "nbformat": 4,
diff --git a/docs/source/tutorials/8_LCA.ipynb b/docs/source/tutorials/8_LCA.ipynb
index 0c4819d8..2e67c6f0 100644
--- a/docs/source/tutorials/8_LCA.ipynb
+++ b/docs/source/tutorials/8_LCA.ipynb
@@ -8,7 +8,7 @@
     "\n",
     "* **Prepared by:**\n",
     "\n",
-    "    - [Yalin Li](https://qsdsan.readthedocs.io/en/latest/authors/Yalin_Li.html)\n",
+    "    - [Yalin Li](https://qsdsan.readthedocs.io/en/latest/CONTRIBUTING.html)\n",
     "\n",
     "* **Covered topics:**\n",
     "\n",
@@ -19,7 +19,7 @@
     "\n",
     "- **Video demo:**\n",
     "\n",
-    "    - [Tori Morgan](https://qsdsan.readthedocs.io/en/latest/authors/Tori_Morgan.html)\n",
+    "    - [Tori Morgan](https://qsdsan.readthedocs.io/en/latest/CONTRIBUTING.html)\n",
     "\n",
     "To run tutorials in your browser, go to this [Binder page](https://mybinder.org/v2/gh/QSD-Group/QSDsan/main?filepath=%2Fdocs%2Fsource%2Ftutorials).\n",
     "\n",
@@ -1148,7 +1148,36 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.13"
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
   }
  },
  "nbformat": 4,
diff --git a/docs/source/tutorials/9_Uncertainty_and_Sensitivity_Analyses.ipynb b/docs/source/tutorials/9_Uncertainty_and_Sensitivity_Analyses.ipynb
index ba80082b..df4a4e32 100644
--- a/docs/source/tutorials/9_Uncertainty_and_Sensitivity_Analyses.ipynb
+++ b/docs/source/tutorials/9_Uncertainty_and_Sensitivity_Analyses.ipynb
@@ -9,7 +9,7 @@
     "\n",
     "* **Prepared by:**\n",
     "\n",
-    "    - [Yalin Li](https://qsdsan.readthedocs.io/en/latest/authors/Yalin_Li.html)\n",
+    "    - [Yalin Li](https://qsdsan.readthedocs.io/en/latest/CONTRIBUTING.html)\n",
     "\n",
     "* **Covered topics:**\n",
     "\n",
@@ -18,7 +18,7 @@
     "    \n",
     "- **Video demo:**\n",
     "\n",
-    "    - [Hannah Lohman](https://qsdsan.readthedocs.io/en/latest/authors/Hannah_Lohman.html)\n",
+    "    - [Hannah Lohman](https://qsdsan.readthedocs.io/en/latest/CONTRIBUTING.html)\n",
     "\n",
     "To run tutorials in your browser, go to this [Binder page](https://mybinder.org/v2/gh/QSD-Group/QSDsan/main?filepath=%2Fdocs%2Fsource%2Ftutorials).\n",
     "\n",
@@ -767,7 +767,36 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.13"
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
   }
  },
  "nbformat": 4,
diff --git a/docs/source/tutorials/12_Chlorination.ipynb b/docs/source/tutorials/TBD_Chlorination.ipynb
similarity index 98%
rename from docs/source/tutorials/12_Chlorination.ipynb
rename to docs/source/tutorials/TBD_Chlorination.ipynb
index 7b416768..8f782219 100644
--- a/docs/source/tutorials/12_Chlorination.ipynb
+++ b/docs/source/tutorials/TBD_Chlorination.ipynb
@@ -28,7 +28,7 @@
    "source": [
     "---\n",
     "### Note\n",
-    "This tutorial is under active development."
+    "This tutorial is stale."
    ]
   },
   {
@@ -1038,7 +1038,36 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.13"
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
   }
  },
  "nbformat": 4,
diff --git a/docs/source/tutorials/Tutorial_11.ipynb b/docs/source/tutorials/Tutorial_11.ipynb
deleted file mode 100644
index 545c1773..00000000
--- a/docs/source/tutorials/Tutorial_11.ipynb
+++ /dev/null
@@ -1,401 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "9b7ba848",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "This tutorial was made with qsdsan v1.2.5 and exposan v1.2.5\n"
-     ]
-    }
-   ],
-   "source": [
-    "import qsdsan as qs, exposan\n",
-    "print(f'This tutorial was made with qsdsan v{qs.__version__} and exposan v{exposan.__version__}')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "a31c8f69",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "System: bsm1_sys\n",
-      "ins...\n",
-      "[0] wastewater\n",
-      "    phase: 'l', T: 293.15 K, P: 101325 Pa\n",
-      "    flow (kmol/hr): S_I   23.1\n",
-      "                    S_S   53.4\n",
-      "                    X_I   39.4\n",
-      "                    X_S   155\n",
-      "                    X_BH  21.7\n",
-      "                    S_NH  1.34\n",
-      "                    S_ND  0.381\n",
-      "                    ...   4.26e+04\n",
-      "outs...\n",
-      "[0] effluent\n",
-      "    phase: 'l', T: 293.15 K, P: 101325 Pa\n",
-      "    flow: 0\n",
-      "[1] WAS\n",
-      "    phase: 'l', T: 293.15 K, P: 101325 Pa\n",
-      "    flow: 0\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Let's load the BSM1 system first\n",
-    "from exposan import bsm1\n",
-    "bsm1.load()\n",
-    "sys = bsm1.sys\n",
-    "sys.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "5fe1776f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/svg+xml": [
-       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"1023pt\" height=\"203pt\" viewBox=\"0.00 0.00 1023.00 202.50\">\n",
-       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 198.5)\">\n",
-       "<!-- A1CSTR&#45;&gt;A2CSTR -->\n",
-       "<g id=\"edge2\" class=\"edge\">\n",
-       "<title>A1CSTR:c-&gt;A2CSTR:c</title>\n",
-       "<path fill=\"none\" stroke=\"#90918e\" stroke-dasharray=\"5,2\" d=\"M173.18,-117.65C186.51,-126.35 202.02,-136.47 216.1,-145.66\"/>\n",
-       "<polygon fill=\"#90918e\" stroke=\"#90918e\" points=\"214.37,-148.71 224.66,-151.24 218.19,-142.85 214.37,-148.71\"/>\n",
-       "<g id=\"a_edge2-label\"><a xlink:title=\"(empty)\">\n",
-       "<text text-anchor=\"middle\" x=\"199\" y=\"-146.8\" font-family=\"Arial\" font-size=\"14.00\" fill=\"#90918e\"> ws1</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- A2CSTR&#45;&gt;O1CSTR -->\n",
-       "<g id=\"edge3\" class=\"edge\">\n",
-       "<title>A2CSTR:c-&gt;O1CSTR:c</title>\n",
-       "<path fill=\"none\" stroke=\"#90918e\" stroke-dasharray=\"5,2\" d=\"M296.03,-173C307.24,-173 319.79,-173 331.65,-173\"/>\n",
-       "<polygon fill=\"#90918e\" stroke=\"#90918e\" points=\"331.86,-176.5 341.86,-173 331.86,-169.5 331.86,-176.5\"/>\n",
-       "<g id=\"a_edge3-label\"><a xlink:title=\"(empty)\">\n",
-       "<text text-anchor=\"middle\" x=\"319\" y=\"-176.8\" font-family=\"Arial\" font-size=\"14.00\" fill=\"#90918e\"> ws3</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- O1CSTR&#45;&gt;O2CSTR -->\n",
-       "<g id=\"edge4\" class=\"edge\">\n",
-       "<title>O1CSTR:c-&gt;O2CSTR:c</title>\n",
-       "<path fill=\"none\" stroke=\"#90918e\" stroke-dasharray=\"5,2\" d=\"M418.2,-173C430.59,-173 444.55,-173 457.6,-173\"/>\n",
-       "<polygon fill=\"#90918e\" stroke=\"#90918e\" points=\"457.82,-176.5 467.82,-173 457.82,-169.5 457.82,-176.5\"/>\n",
-       "<g id=\"a_edge4-label\"><a xlink:title=\"(empty)\">\n",
-       "<text text-anchor=\"middle\" x=\"443\" y=\"-176.8\" font-family=\"Arial\" font-size=\"14.00\" fill=\"#90918e\"> ws5</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- O2CSTR&#45;&gt;O3CSTR -->\n",
-       "<g id=\"edge5\" class=\"edge\">\n",
-       "<title>O2CSTR:c-&gt;O3CSTR:c</title>\n",
-       "<path fill=\"none\" stroke=\"#90918e\" stroke-dasharray=\"5,2\" d=\"M540.75,-151.35C554.29,-142.65 570.07,-132.53 584.39,-123.34\"/>\n",
-       "<polygon fill=\"#90918e\" stroke=\"#90918e\" points=\"586.57,-126.1 593.09,-117.76 582.78,-120.21 586.57,-126.1\"/>\n",
-       "<g id=\"a_edge5-label\"><a xlink:title=\"(empty)\">\n",
-       "<text text-anchor=\"middle\" x=\"567\" y=\"-146.8\" font-family=\"Arial\" font-size=\"14.00\" fill=\"#90918e\"> ws7</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- O3CSTR&#45;&gt;A1CSTR -->\n",
-       "<g id=\"edge7\" class=\"edge\">\n",
-       "<title>O3CSTR:c-&gt;A1CSTR:c</title>\n",
-       "<path fill=\"none\" stroke=\"#90918e\" stroke-dasharray=\"5,2\" d=\"M589.97,-96C566.25,-96 534.81,-96 507,-96 258,-96 258,-96 258,-96 234.41,-96 208.16,-96 186.43,-96\"/>\n",
-       "<polygon fill=\"#90918e\" stroke=\"#90918e\" points=\"186.15,-92.5 176.15,-96 186.15,-99.5 186.15,-92.5\"/>\n",
-       "<g id=\"a_edge7-label\"><a xlink:title=\"(empty)\">\n",
-       "<text text-anchor=\"middle\" x=\"380\" y=\"-99.8\" font-family=\"Arial\" font-size=\"14.00\" fill=\"#90918e\"> RWW</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- O3CSTR&#45;&gt;C1Flat bottom circular clarifier -->\n",
-       "<g id=\"edge9\" class=\"edge\">\n",
-       "<title>O3CSTR:c-&gt;C1Flat bottom circular clarifier:c</title>\n",
-       "<path fill=\"none\" stroke=\"#90918e\" stroke-dasharray=\"5,2\" d=\"M666.33,-84.81C689.93,-77.74 721.4,-68.3 750.6,-59.55\"/>\n",
-       "<polygon fill=\"#90918e\" stroke=\"#90918e\" points=\"751.83,-62.84 760.4,-56.61 749.82,-56.13 751.83,-62.84\"/>\n",
-       "<g id=\"a_edge9-label\"><a xlink:title=\"(empty)\">\n",
-       "<text text-anchor=\"middle\" x=\"698.5\" y=\"-85.8\" font-family=\"Arial\" font-size=\"14.00\" fill=\"#90918e\"> treated</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- C1Flat bottom circular clarifier&#45;&gt;A1CSTR -->\n",
-       "<g id=\"edge8\" class=\"edge\">\n",
-       "<title>C1Flat bottom circular clarifier:c-&gt;A1CSTR:c</title>\n",
-       "<path fill=\"none\" stroke=\"#90918e\" stroke-dasharray=\"5,2\" d=\"M730.97,-14.08C698.54,-9 662.39,-5 629,-5 258,-5 258,-5 258,-5 217.15,-5 181.8,-39.62 160.7,-66.16\"/>\n",
-       "<polygon fill=\"#90918e\" stroke=\"#90918e\" points=\"157.67,-64.37 154.37,-74.43 163.23,-68.62 157.67,-64.37\"/>\n",
-       "<g id=\"a_edge8-label\"><a xlink:title=\"(empty)\">\n",
-       "<text text-anchor=\"middle\" x=\"443\" y=\"-8.8\" font-family=\"Arial\" font-size=\"14.00\" fill=\"#90918e\"> RAS</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- C1Flat bottom circular clarifier&#45;&gt; effluent -->\n",
-       "<g id=\"edge6\" class=\"edge\">\n",
-       "<title>C1Flat bottom circular clarifier:c-&gt; effluent:w</title>\n",
-       "<path fill=\"none\" stroke=\"#90918e\" stroke-dasharray=\"5,2\" d=\"M931.71,-56.51C955.71,-60.27 981.43,-63 1005.5,-63\"/>\n",
-       "<g id=\"a_edge6-label\"><a xlink:title=\"(empty)\">\n",
-       "<text text-anchor=\"middle\" x=\"970\" y=\"-66.8\" font-family=\"Arial\" font-size=\"14.00\" fill=\"#90918e\"> effluent</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- C1Flat bottom circular clarifier&#45;&gt; WAS -->\n",
-       "<g id=\"edge10\" class=\"edge\">\n",
-       "<title>C1Flat bottom circular clarifier:c-&gt; WAS:w</title>\n",
-       "<path fill=\"none\" stroke=\"#90918e\" stroke-dasharray=\"5,2\" d=\"M931.71,-13.49C955.71,-9.73 981.43,-7 1005.5,-7\"/>\n",
-       "<g id=\"a_edge10-label\"><a xlink:title=\"(empty)\">\n",
-       "<text text-anchor=\"middle\" x=\"970\" y=\"-15.8\" font-family=\"Arial\" font-size=\"14.00\" fill=\"#90918e\"> WAS</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!--  wastewater&#45;&gt;A1CSTR -->\n",
-       "<g id=\"edge1\" class=\"edge\">\n",
-       "<title> wastewater:e-&gt;A1CSTR:c</title>\n",
-       "<path fill=\"none\" stroke=\"#90918e\" d=\"M11,-96C41.65,-96 76.46,-96 101.87,-96\"/>\n",
-       "<g id=\"a_edge1-label\"><a xlink:title=\"Temperature: 293 KPressure: 101325 PaLiquid flow: 4.29e+04 kmol/hr\">\n",
-       "<text text-anchor=\"middle\" x=\"56.5\" y=\"-99.8\" font-family=\"Arial\" font-size=\"14.00\" fill=\"#90918e\"> wastewater</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- A1CSTR -->\n",
-       "<g id=\"node1\" class=\"node\">\n",
-       "<title>A1CSTR</title>\n",
-       "<g id=\"a_node1\"><a xlink:title=\"No capital costs or utilities\">\n",
-       "<polygon fill=\"#555f69\" stroke=\"#90918e\" points=\"176,-117.5 102,-117.5 102,-74.5 176,-74.5 176,-117.5\"/>\n",
-       "<text text-anchor=\"middle\" x=\"139\" y=\"-92.3\" font-family=\"Arial\" font-size=\"14.00\" fill=\"white\">A1CSTR</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- A2CSTR -->\n",
-       "<g id=\"node2\" class=\"node\">\n",
-       "<title>A2CSTR</title>\n",
-       "<g id=\"a_node2\"><a xlink:title=\"No capital costs or utilities\">\n",
-       "<polygon fill=\"#555f69\" stroke=\"#90918e\" points=\"296,-194.5 222,-194.5 222,-151.5 296,-151.5 296,-194.5\"/>\n",
-       "<text text-anchor=\"middle\" x=\"259\" y=\"-169.3\" font-family=\"Arial\" font-size=\"14.00\" fill=\"white\">A2CSTR</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- O1CSTR -->\n",
-       "<g id=\"node3\" class=\"node\">\n",
-       "<title>O1CSTR</title>\n",
-       "<g id=\"a_node3\"><a xlink:title=\"No capital costs or utilities\">\n",
-       "<polygon fill=\"#555f69\" stroke=\"#90918e\" points=\"418,-194.5 342,-194.5 342,-151.5 418,-151.5 418,-194.5\"/>\n",
-       "<text text-anchor=\"middle\" x=\"380\" y=\"-169.3\" font-family=\"Arial\" font-size=\"14.00\" fill=\"white\">O1CSTR</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- O2CSTR -->\n",
-       "<g id=\"node4\" class=\"node\">\n",
-       "<title>O2CSTR</title>\n",
-       "<g id=\"a_node4\"><a xlink:title=\"No capital costs or utilities\">\n",
-       "<polygon fill=\"#555f69\" stroke=\"#90918e\" points=\"544,-194.5 468,-194.5 468,-151.5 544,-151.5 544,-194.5\"/>\n",
-       "<text text-anchor=\"middle\" x=\"506\" y=\"-169.3\" font-family=\"Arial\" font-size=\"14.00\" fill=\"white\">O2CSTR</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- O3CSTR -->\n",
-       "<g id=\"node5\" class=\"node\">\n",
-       "<title>O3CSTR</title>\n",
-       "<g id=\"a_node5\"><a xlink:title=\"No capital costs or utilities\">\n",
-       "<polygon fill=\"#555f69\" stroke=\"#90918e\" points=\"666,-117.5 590,-117.5 590,-74.5 666,-74.5 666,-117.5\"/>\n",
-       "<text text-anchor=\"middle\" x=\"628\" y=\"-92.3\" font-family=\"Arial\" font-size=\"14.00\" fill=\"white\">O3CSTR</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- C1Flat bottom circular clarifier -->\n",
-       "<g id=\"node6\" class=\"node\">\n",
-       "<title>C1Flat bottom circular clarifier</title>\n",
-       "<g id=\"a_node6\"><a xlink:title=\"No capital costs or utilities\">\n",
-       "<polygon fill=\"#555f69\" stroke=\"#90918e\" points=\"936,-56.5 731,-56.5 731,-13.5 936,-13.5 936,-56.5\"/>\n",
-       "<text text-anchor=\"middle\" x=\"833.5\" y=\"-31.3\" font-family=\"Arial\" font-size=\"14.00\" fill=\"white\">C1Flat bottom circular clarifier</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!--  wastewater -->\n",
-       "<g id=\"node7\" class=\"node\">\n",
-       "<title> wastewater</title>\n",
-       "<polygon fill=\"#f98f60\" stroke=\"#90918e\" points=\"5.5,-101.5 0,-96 5.5,-90.5 11,-96 5.5,-101.5\"/>\n",
-       "</g>\n",
-       "<!--  effluent -->\n",
-       "<g id=\"node8\" class=\"node\">\n",
-       "<title> effluent</title>\n",
-       "<polygon fill=\"#f98f60\" stroke=\"#90918e\" points=\"1015,-63 1006.75,-70 1006.75,-56 1015,-63\"/>\n",
-       "</g>\n",
-       "<!--  WAS -->\n",
-       "<g id=\"node9\" class=\"node\">\n",
-       "<title> WAS</title>\n",
-       "<polygon fill=\"#f98f60\" stroke=\"#90918e\" points=\"1015,-7 1006.75,-14 1006.75,0 1015,-7\"/>\n",
-       "</g>\n",
-       "</g>\n",
-       "</svg>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.SVG object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# The BSM1 system is composed of 5 CSTRs in series,\n",
-    "# followed by a flat-bottom circular clarifier.\n",
-    "sys.diagram()\n",
-    "# sys.units"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "98d2662c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# We can verify that by\n",
-    "sys.isdynamic"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "e2c64ce0",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{<CSTR: A1>: True,\n",
-       " <CSTR: A2>: True,\n",
-       " <CSTR: O1>: True,\n",
-       " <CSTR: O2>: True,\n",
-       " <CSTR: O3>: True,\n",
-       " <FlatBottomCircularClarifier: C1>: True}"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# This is because the system contains at least one dynamic SanUnit\n",
-    "{u: u.isdynamic for u in sys.units}\n",
-    "\n",
-    "# If we disable dynamic simulation, then `simulate` would work as usual\n",
-    "# sys.isdynamic = False\n",
-    "# sys.simulate()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d8cc6e48",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\joy_c\\anaconda3\\envs\\tut\\lib\\site-packages\\qsdsan\\sanunits\\_suspended_growth_bioreactor.py:44: NumbaPerformanceWarning: \u001b[1m\u001b[1m'@' is faster on contiguous arrays, called on (array(float64, 1d, A), array(float64, 2d, A))\u001b[0m\u001b[0m\n",
-      "  flow_in = Q_ins @ C_ins / V_arr\n",
-      "C:\\Users\\joy_c\\anaconda3\\envs\\tut\\lib\\site-packages\\numba\\core\\typing\\npydecl.py:913: NumbaPerformanceWarning: \u001b[1m'@' is faster on contiguous arrays, called on (array(float64, 1d, A), array(float64, 2d, A))\u001b[0m\n",
-      "  warnings.warn(NumbaPerformanceWarning(msg))\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Let's try simulating the BSM1 system from day 0 to day 50\n",
-    "sys.simulate(t_span=(0, 50), method='BDF', state_reset_hook='reset_cache')\n",
-    "sys.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ba51c0b9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# This shows the units/streams whose state variables are kept track of\n",
-    "# during dynamic simulations.\n",
-    "sys.scope.subjects"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c4c0bdfd",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f1d690bf",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c05808bc",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "37df12a9",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python [conda env:tut]",
-   "language": "python",
-   "name": "conda-env-tut-py"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/docs/source/tutorials/_index.rst b/docs/source/tutorials/_index.rst
index 6fa4cbe7..c32424f2 100644
--- a/docs/source/tutorials/_index.rst
+++ b/docs/source/tutorials/_index.rst
@@ -28,6 +28,7 @@ Topical Tutorials
    9_Uncertainty_and_Sensitivity_Analyses
    10_Process
    11_Dynamic_Simulation
+   12_Anaerobic_Digestion_Model_No_1
 
 
 Additional Resources
diff --git a/docs/source/tutorials/adm1.jpg b/docs/source/tutorials/adm1.jpg
new file mode 100644
index 00000000..f3d7dd98
Binary files /dev/null and b/docs/source/tutorials/adm1.jpg differ
diff --git a/qsdsan/_impact_item.py b/qsdsan/_impact_item.py
index 98fc476a..395bfb1c 100644
--- a/qsdsan/_impact_item.py
+++ b/qsdsan/_impact_item.py
@@ -414,7 +414,7 @@ def load_from_file(cls, path_or_dict, index_col=None):
         This Excel should have multiple sheets:
 
             - The "info" sheet should have three columns: "ID" (e.g., Cement) \
-             "functional_unit" (e.g., kg), and "kind" ("ImpactItem" or "StreamImpactItem") 
+             "functional_unit" (e.g., kg), and "kind" ("ImpactItem" or "StreamImpactItem") \
              of different impact items.
 
             - The remaining sheets should contain characterization factors of \
diff --git a/qsdsan/processes/_kinetic_reaction.py b/qsdsan/processes/_kinetic_reaction.py
index 565d2da2..708804fc 100644
--- a/qsdsan/processes/_kinetic_reaction.py
+++ b/qsdsan/processes/_kinetic_reaction.py
@@ -118,8 +118,9 @@ class KineticReaction(Rxn):
     0.037578/2.7183**(2.2e-5*t)
     >>> round(rxn.half_life, 2)
     31506.69
-    >>> # You can also look at the conversion over time
-    >>> fig = rxn.plot_conversion_over_time()
+    >>> # You can also look at the conversion over time,
+    >>> # set `show` to "True" or use fig.show() to see the figure
+    >>> fig = rxn.plot_conversion_over_time(show=False)
         
     References
     ----------
@@ -183,7 +184,8 @@ def plot_conversion_over_time(self, **kwargs):
         All keyword arguments will be passed to :func:`sympy.plot`.
         '''
         ylabel = kwargs.pop('ylabel', 'Conversion')
-        plot(self._X_t, (self._t_sym, 0, self.t), ylabel=ylabel)
+        fig = plot(self._X_t, (self._t_sym, 0, self.t), ylabel=ylabel, **kwargs)
+        return fig
         
     @property
     def rate_reactant(self):
diff --git a/qsdsan/sanunits/_non_reactive.py b/qsdsan/sanunits/_non_reactive.py
index db5d4cde..4b3aec98 100644
--- a/qsdsan/sanunits/_non_reactive.py
+++ b/qsdsan/sanunits/_non_reactive.py
@@ -5,6 +5,7 @@
 QSDsan: Quantitative Sustainable Design for sanitation and resource recovery systems
 
 This module is developed by:
+
     Yalin Li <mailto.yalin.li@gmail.com>
 
 This module is under the University of Illinois/NCSA Open Source License.
@@ -92,11 +93,12 @@ def __init__(self, ID='', ins=None, outs=(), thermo=None, init_with='WasteStream
         Copier.__init__(self, ID, ins, outs, thermo, init_with)
         self.F_BM = {cost_item_name: 1}
         self.CAPEX_dct = {cost_item_name: CAPEX}
-        self.power_utility(power)
+        self.power = power
         self._add_OPEX = add_OPEX
         for attr, val in kwargs.items():
             setattr(self, attr, val)
 
 
     def _cost(self):
-        self.baseline_purchase_costs.update(self.CAPEX_dct)
\ No newline at end of file
+        self.baseline_purchase_costs.update(self.CAPEX_dct)
+        self.power_utility.consumption = self.power
\ No newline at end of file
diff --git a/tests/test_exposan.py b/tests/test_exposan.py
index b716f8d5..5be8c89d 100644
--- a/tests/test_exposan.py
+++ b/tests/test_exposan.py
@@ -54,7 +54,7 @@ def test_exposan():
     br.load()
     br.print_summaries((br.sysA, br.sysB, br.sysC, br.sysD))
 
-    clear_lca_registries()    
+    clear_lca_registries()
     from exposan import bwaise as bw
     bw.load()
     bw.print_summaries((bw.sysA, bw.sysB, bw.sysC))
@@ -67,11 +67,29 @@ def test_exposan():
     clear_lca_registries()
     from exposan import htl
     htl.load()
+    
+    clear_lca_registries()
+    from exposan import metab
+    UASB_M = metab.create_system(n_stages=2, reactor_type='UASB', gas_extraction='M', tot_HRT=4)
+    UASB_M.simulate(state_reset_hook='reset_cache', method='BDF', t_span=(0, 400))
+    FB_H = metab.create_system(n_stages=2, reactor_type='FB', gas_extraction='H', tot_HRT=4)
+    # # Just simulate one system to save testing time
+    # # (all configurations are included in EXPOsan test)
+    # FB_H.simulate(state_reset_hook='reset_cache', method='BDF', t_span=(0, 400))
+    PB_P = metab.create_system(n_stages=2, reactor_type='PB', gas_extraction='P', tot_HRT=4)
+    # Might fail the first time it runs, re-running will usually fix the problem
+    # try: PB_P.simulate(state_reset_hook='reset_cache', method='BDF', t_span=(0, 400))
+    # except: PB_P.simulate(state_reset_hook='reset_cache', method='BDF', t_span=(0, 400))
 
     clear_lca_registries()
     from exposan import reclaimer as re
     re.load()
     re.print_summaries((re.sysA, re.sysB, re.sysC, re.sysD))
+    
+    clear_lca_registries()
+    from exposan import pou_disinfection as pou
+    pou.load()
+    pou.print_summaries((pou.sysA, pou.sysB, pou.sysC, pou.sysD))
 
 
 if __name__ == '__main__':