{
"cells": [
{
"cell_type": "markdown",
"id": "28c4658c",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Dynamic Simulation \n",
"\n",
"*Click the badge below to try this tutorial interactively in your browser:*\n",
"\n",
"[](https://mybinder.org/v2/gh/QSD-Group/QSDsan-env/main?urlpath=git-pull%3Frepo%3Dhttps%253A%252F%252Fgithub.com%252FQSD-group%252FQSDsan%26urlpath%3Dlab%252Ftree%252FQSDsan%252Fdocs%252Fsource%252Ftutorials%26branch%3Dmain)\n",
"\n",
"*You can also run this tutorial in [Google Colab](https://colab.research.google.com). It takes a one-time setup per session: follow the [Colab instructions](https://qsdsan.readthedocs.io/en/latest/tutorials/index.html#run-in-colab).*\n",
"\n",
"- **Prepared by:**\n",
"\n",
" - [Joy Zhang](https://github.com/joyxyz1994/)\n",
"\n",
"- **Learning objectives.** After this tutorial, you will be able to:\n",
"\n",
" - Set up and run a dynamic simulation\n",
" - Manage state variables and time-series outputs\n",
" - Save and inspect dynamic results\n",
"\n",
"- **Prerequisites:** [10. Process](https://qsdsan.readthedocs.io/en/latest/tutorials/10_Process.html)\n",
"\n",
"- **Covered topics:**\n",
"\n",
" - 1. Understanding dynamic simulation with QSDsan\n",
" - 2. Writing a dynamic SanUnit\n",
" - 3. Other convenient features\n",
"\n",
"> **Companion video.** A walkthrough of this tutorial is available on [YouTube](https://youtu.be/1Rr1QxUiE5k), presented by [Yalin Li](https://github.com/yalinli2). Recorded against `QSDsan` v1.3.1. The concepts still apply, but if the code on screen differs from this notebook, follow the notebook.\n"
]
},
{
"cell_type": "markdown",
"id": "b4f9dac9697a",
"metadata": {},
"source": [
"\n",
"\n",
"## Setup\n",
"\n",
"Import `QSDsan` and confirm the installed version.\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "3dc1138e",
"metadata": {
"execution": {
"iopub.execute_input": "2026-05-30T20:37:45.750819Z",
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"slide_type": "slide"
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"This tutorial was made with qsdsan v1.5.3 and exposan v1.5.3\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": "b7f9ccfc",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## 1. Understanding dynamic simulation with QSDsan "
]
},
{
"cell_type": "markdown",
"id": "2bc790e7",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"From previous tutorials, we've covered how to use QSDsan's [SanUnit](https://qsdsan.readthedocs.io/en/latest/tutorials/5_SanUnit_advanced.html) and [WasteStream](https://qsdsan.readthedocs.io/en/latest/tutorials/3_WasteStream.html) classes to model the mass/energy flows throughout a system. You may have noticed, the simulation results generated by `SanUnit._run` are **static**, i.e., they don't carry time-related information. \n",
"\n",
"In this tutorial, we will learn about the **dynamic** simulation features in QSDsan. First we will focus on performing dynamic simulations with an existing system to understand the basics. Then we'll go over how to implement your own algorithms for dynamic simulations. "
]
},
{
"cell_type": "markdown",
"id": "5e4d0755",
"metadata": {},
"source": [
"### 1.1. An example system\n",
"Let's use [Benchmark Simulation Model no.1 (BSM1)](https://iwa-mia.org/benchmarking/#BSM1) as an example. BSM1 describes an activated sludge treatment process that can be commonly found in conventional wastewater treatment facilities. It uses the same activated-sludge layout (two anoxic tanks, three aerated tanks, and a clarifier with two recycles) built and illustrated in [6. System](https://qsdsan.readthedocs.io/en/latest/tutorials/6_System.html); here we load the full implementation, with process models, from [EXPOsan](https://github.com/QSD-Group/EXPOsan/tree/main/exposan/bsm1)."
]
},
{
"cell_type": "markdown",
"id": "a8a07c91",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"#### 1.1.1. Running dynamic simulation"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "a1c82016",
"metadata": {
"execution": {
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"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
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"source": [
"# Let's load the BSM1 system first\n",
"from exposan import bsm1\n",
"bsm1.load()\n",
"sys = bsm1.sys\n",
"\n",
"# The BSM1 system is composed of 5 CSTRs in series, \n",
"# followed by a flat-bottom circular clarifier.\n",
"# sys.units\n",
"sys.diagram()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "03c7b593",
"metadata": {
"execution": {
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"slideshow": {
"slide_type": "slide"
},
"tags": [
"raises-exception"
]
},
"outputs": [
{
"ename": "TypeError",
"evalue": "solve_ivp() missing 1 required positional argument: 't_span'",
"output_type": "error",
"traceback": [
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
"\u001b[31mTypeError\u001b[39m Traceback (most recent call last)",
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;66;03m# If we try to simulate it like we'd do for a \"static\" system\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m sys.simulate()\n",
"\u001b[36mFile \u001b[39m\u001b[32m~\\Documents\\Coding\\QSDsan-platform\\.venv\\Lib\\site-packages\\biosteam\\_system.py:3374\u001b[39m, in \u001b[36mSystem.simulate\u001b[39m\u001b[34m(self, update_configuration, units, design_and_cost, **kwargs)\u001b[39m\n\u001b[32m 3354\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34msimulate\u001b[39m(\u001b[38;5;28mself\u001b[39m, update_configuration: Optional[\u001b[38;5;28mbool\u001b[39m]=\u001b[38;5;28;01mNone\u001b[39;00m, units=\u001b[38;5;28;01mNone\u001b[39;00m, \n\u001b[32m 3355\u001b[39m design_and_cost=\u001b[38;5;28;01mNone\u001b[39;00m, **kwargs):\n\u001b[32m 3356\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 3357\u001b[39m \u001b[33;03m If system is dynamic, run the system dynamically. Otherwise, converge \u001b[39;00m\n\u001b[32m 3358\u001b[39m \u001b[33;03m the path of unit operations to steady state. After running/converging \u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 3372\u001b[39m \u001b[33;03m \u001b[39;00m\n\u001b[32m 3373\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m3374\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m.flowsheet:\n\u001b[32m 3375\u001b[39m specifications = \u001b[38;5;28mself\u001b[39m._specifications\n\u001b[32m 3376\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m specifications \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m._running_specifications:\n",
"\u001b[36mFile \u001b[39m\u001b[32m~\\Documents\\Coding\\QSDsan-platform\\.venv\\Lib\\site-packages\\biosteam\\_flowsheet.py:120\u001b[39m, in \u001b[36mFlowsheet.__exit__\u001b[39m\u001b[34m(self, type, exception, traceback)\u001b[39m\n\u001b[32m 118\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m__exit__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28mtype\u001b[39m, exception, traceback):\n\u001b[32m 119\u001b[39m main_flowsheet.set_flowsheet(\u001b[38;5;28mself\u001b[39m._temporary_stack.pop())\n\u001b[32m--> \u001b[39m\u001b[32m120\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m exception: \u001b[38;5;28;01mraise\u001b[39;00m exception\n",
"\u001b[36mFile \u001b[39m\u001b[32m~\\Documents\\Coding\\QSDsan-platform\\.venv\\Lib\\site-packages\\biosteam\\_system.py:3418\u001b[39m, in \u001b[36mSystem.simulate\u001b[39m\u001b[34m(self, update_configuration, units, design_and_cost, **kwargs)\u001b[39m\n\u001b[32m 3416\u001b[39m \u001b[38;5;28mself\u001b[39m._setup(update_configuration, units)\n\u001b[32m 3417\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.isdynamic: \n\u001b[32m-> \u001b[39m\u001b[32m3418\u001b[39m outputs = \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mdynamic_run\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mkwargs\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 3419\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m design_and_cost: \u001b[38;5;28mself\u001b[39m._summary()\n\u001b[32m 3420\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n",
"\u001b[36mFile \u001b[39m\u001b[32m~\\Documents\\Coding\\QSDsan-platform\\.venv\\Lib\\site-packages\\biosteam\\_system.py:3513\u001b[39m, in \u001b[36mSystem.dynamic_run\u001b[39m\u001b[34m(self, **dynsim_kwargs)\u001b[39m\n\u001b[32m 3511\u001b[39m \u001b[38;5;66;03m# Integrate\u001b[39;00m\n\u001b[32m 3512\u001b[39m \u001b[38;5;28mself\u001b[39m.dynsim_kwargs[\u001b[33m'\u001b[39m\u001b[33mprint_t\u001b[39m\u001b[33m'\u001b[39m] = print_t \u001b[38;5;66;03m# self.dynsim_kwargs might be reset by `state_reset_hook`\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m3513\u001b[39m \u001b[38;5;28mself\u001b[39m.scope.sol = sol = \u001b[30;43msolve_ivp\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mfun\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mDAE\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43my0\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43my0\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mdk_cp\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 3514\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m print_msg:\n\u001b[32m 3515\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m sol.status == \u001b[32m0\u001b[39m:\n",
"\u001b[31mTypeError\u001b[39m: solve_ivp() missing 1 required positional argument: 't_span'"
]
}
],
"source": [
"# If we try to simulate it like we'd do for a \"static\" system\n",
"sys.simulate()"
]
},
{
"cell_type": "markdown",
"id": "07f91f64",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"We run into this error because QSDsan (essentially biosteam in the background) considers this system dynamic, and additional arguments are required for `simulate` to work."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "b349b9a3",
"metadata": {
"execution": {
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"shell.execute_reply": "2026-05-30T20:38:03.366079Z"
},
"slideshow": {
"slide_type": "slide"
}
},
"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": "985f9b58",
"metadata": {
"execution": {
"iopub.execute_input": "2026-05-30T20:38:03.366079Z",
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"shell.execute_reply": "2026-05-30T20:38:03.373470Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"{: True,\n",
" : True,\n",
" : True,\n",
" : True,\n",
" : True,\n",
" : True}"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# This is because the system contains at least one dynamic SanUnit\n",
"{u: u.isdynamic for u in sys.units}"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "43772dae",
"metadata": {
"execution": {
"iopub.execute_input": "2026-05-30T20:38:03.376619Z",
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"shell.execute_reply": "2026-05-30T20:38:03.382618Z"
},
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"System: bsm1_sys\n",
"Highest convergence error among components in recycle\n",
"streams {C1-1, O3-0} after 1 loops:\n",
"- flow rate 1.46e-11 kmol/hr (2.7e-14%)\n",
"- temperature 0.00e+00 K (0%)\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 1.73\n",
" S_ND 5.34\n",
" ... 4.26e+04\n",
"outs...\n",
"[0] effluent \n",
" phase: 'l', T: 293.15 K, P: 101325 Pa\n",
" flow (kmol/hr): S_I 22.6\n",
" S_S 52.3\n",
" X_I 38.5\n",
" X_S 152\n",
" X_BH 21.2\n",
" S_NH 1.7\n",
" S_ND 5.23\n",
" ... 4.17e+04\n",
"[1] WAS \n",
" phase: 'l', T: 293.15 K, P: 101325 Pa\n",
" flow (kmol/hr): S_I 0.481\n",
" S_S 1.11\n",
" X_I 0.821\n",
" X_S 3.25\n",
" X_BH 0.452\n",
" S_NH 0.0361\n",
" S_ND 0.111\n",
" ... 888\n"
]
}
],
"source": [
"# If we disable dynamic simulation, then `simulate` would work as usual\n",
"sys.isdynamic = False\n",
"sys.simulate()\n",
"sys.show()"
]
},
{
"cell_type": "markdown",
"id": "cc28e85f",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"To perform a dynamic simulation of the system, we need to provide at least one additional keyword argument, i.e., `t_span`, as suggested in the error message. `t_span` is a 2-tuple indicating the simulation period.\n",
"\n",
"
\n",
"\n",
"**Note:** Whether `t_span = (0,10)` means 0-10 days or 0-10 hours/minutes/months depends entirely on units of the parameters in the system's ODEs. For BSM1, it'd mean 0-10 days because all parameters in the ODEs express time in the unit of \"day\".\n",
"\n",
"
"
]
},
{
"cell_type": "markdown",
"id": "0c111c81",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"Other often-used keyword arguments include:\n",
"\n",
"- `t_eval`: a 1d array to specify the output time points\n",
"- `method`: a string specifying the ordinary differential equation (ODE) solver\n",
"- `atol` and `rtol`: the absolute and relative error tolerances the solver holds each step to; tighten them (smaller values) if a trajectory looks under-resolved or jagged, or loosen them to trade accuracy for speed\n",
"- `state_reset_hook`: controls what happens to the system's dynamic state *before* integration starts. Common values:\n",
" - `'reset_cache'` — clear the compiled DAE plus every unit's and stream's `_state` / `_dstate`, then re-initialize from the static-converged design. Use this when you want each `sys.simulate(...)` call to behave like a fresh first run (e.g., after changing influent characteristics, kinetic parameters, or unit sizes between simulations). It's the safe default when you're not sure.\n",
" - `'clear_state'` — lighter-weight: zero out unit and stream state arrays but keep the compiled DAE. Useful when only the initial conditions changed.\n",
" - `None` (default) — leave existing state intact. The next simulation **resumes** from wherever the previous one left off — useful for stitching successive time windows together, or for warm-starting from a converged state.\n",
"\n",
"`t_span`, `t_eval`, `method`, `atol`, and `rtol` are essentially passed to [scipy.integrate.solve_ivp](https://docs.scipy.org/doc/scipy/reference/generated/scipy.integrate.solve_ivp.html) as keyword arguments. See the [System.dynamic_run documentation](https://biosteam.readthedocs.io/en/latest/API/System.html#biosteam.System.dynamic_run) for a complete list of keyword arguments (it also accepts `print_msg=True`, which prints the solver's completion or failure message, useful when a run does not finish). You may notice that `scipy.integrate.solve_ivp` also requires input of `fun` (i.e., the ODEs) and `y0` (i.e., the initial condition); we'll come back to how `System.simulate` automates the compilation of these inputs in [§2.1](#2.1.-How-the-integrator-drives-a-dynamic-unit)."
]
},
{
"cell_type": "markdown",
"id": "9c8b4556",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"
\n",
"\n",
"**Tip:** For systems that are expected to converge to some sort of \"steady state\", it is usually faster to simulate with implicit ODE solvers (e.g., `method = BDF` or `method = LSODA`) than with explicit ones. If one solver fails to complete integration through the entire specified simulation period, always try with alternative ones. See [scipy.integrate.solve_ivp](https://docs.scipy.org/doc/scipy/reference/generated/scipy.integrate.solve_ivp.html) for the full list of methods and guidance on choosing among them.\n",
"\n",
"
"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "45ef4032",
"metadata": {
"execution": {
"iopub.execute_input": "2026-05-30T20:38:03.386129Z",
"iopub.status.busy": "2026-05-30T20:38:03.386129Z",
"iopub.status.idle": "2026-05-30T20:38:04.963120Z",
"shell.execute_reply": "2026-05-30T20:38:04.962139Z"
},
"scrolled": true,
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"System: bsm1_sys\n",
"Highest convergence error among components in recycle\n",
"streams {C1-1, O3-0} after 5 loops:\n",
"- flow rate 1.46e-11 kmol/hr (4e-14%)\n",
"- temperature 0.00e+00 K (0%)\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 1.73\n",
" S_ND 5.34\n",
" ... 4.26e+04\n",
"outs...\n",
"[0] effluent \n",
" phase: 'l', T: 293.15 K, P: 101325 Pa\n",
" flow (kmol/hr): S_I 22.6\n",
" S_S 0.67\n",
" X_I 3.3\n",
" X_S 0.142\n",
" X_BH 7.36\n",
" X_BA 0.43\n",
" X_P 1.3\n",
" ... 4.17e+04\n",
"[1] WAS \n",
" phase: 'l', T: 293.15 K, P: 101325 Pa\n",
" flow (kmol/hr): S_I 0.481\n",
" S_S 0.0143\n",
" X_I 36\n",
" X_S 1.55\n",
" X_BH 80.3\n",
" X_BA 4.69\n",
" X_P 14.1\n",
" ... 884\n"
]
}
],
"source": [
"# Let's try simulating the BSM1 system from day 0 to day 50 in the dynamic mode.\n",
"# Use shorter time or try changing method to 'RK23' (explicit solver) if it takes a long time\n",
"sys.isdynamic = True\n",
"sys.simulate(t_span=(0, 50), method='BDF', state_reset_hook='reset_cache')\n",
"sys.show()"
]
},
{
"cell_type": "markdown",
"id": "972442da",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"#### 1.1.2. Retrieve dynamic simulation data\n",
"The `show` method only displays the system's state at the end of the simulation period. How do we retrieve information on system dynamics? QSDsan uses [Scope](https://qsdsan.readthedocs.io/en/latest/api/utility_functions/scope.html) objects to keep track of values of state variables during simulation."
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "3d7a8b0d",
"metadata": {
"execution": {
"iopub.execute_input": "2026-05-30T20:38:04.965246Z",
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"shell.execute_reply": "2026-05-30T20:38:04.969278Z"
},
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(, )"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"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": 9,
"id": "5fedeb57",
"metadata": {
"execution": {
"iopub.execute_input": "2026-05-30T20:38:04.970797Z",
"iopub.status.busy": "2026-05-30T20:38:04.970797Z",
"iopub.status.idle": "2026-05-30T20:38:04.981610Z",
"shell.execute_reply": "2026-05-30T20:38:04.981610Z"
},
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# We see that A1 and effluent are tracked, so we can retrieve their \n",
"# time series data through their `scope` attribute, which stores a \n",
"# `SanUnitScope` for unit operations\n",
"A1 = sys.flowsheet.unit.A1\n",
"A1.scope\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "26a40a92",
"metadata": {
"execution": {
"iopub.execute_input": "2026-05-30T20:38:04.983802Z",
"iopub.status.busy": "2026-05-30T20:38:04.983802Z",
"iopub.status.idle": "2026-05-30T20:38:04.987819Z",
"shell.execute_reply": "2026-05-30T20:38:04.987819Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Or `WasteStreamScope` object for streams\n",
"eff = sys.flowsheet.stream.effluent\n",
"eff.scope"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "a7c7fa4d",
"metadata": {
"execution": {
"iopub.execute_input": "2026-05-30T20:38:04.990045Z",
"iopub.status.busy": "2026-05-30T20:38:04.990045Z",
"iopub.status.idle": "2026-05-30T20:38:05.209402Z",
"shell.execute_reply": "2026-05-30T20:38:05.209402Z"
},
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
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FSGAAAAAAAAAAAIqQwAAAAAAAAAAAFCGBAQAAAAAAAAAoQgIDAAAAAAAAAFCEBAYAAAAAAAAAoAgJDAAAAAAAAABAERIYAAAAAAAAAIAiJDAAAAAAAAAAAEVIYAAAAAAAAAAAipDAAAAAAAAAAAAUIYEBAAAAAAAAAChCAgMAAAAAAAAAUIQEBgAAAAAAAACgCAkMAAAAAAAAAEAREhgAAAAAAAAAgCIkMAAAAAAAAAAARUhgAAAAAAAAAACKkMAAAAAAAAAAABQhgQEAAAAAAAAAKEICAwAAAAAAAABQhAQGAAAAAAAAACAUn/8PDXmi2/wsjCcAAAAASUVORK5CYII=",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# `Scope` objects include a function for convenient visualization of time-series data\n",
"fig, ax = A1.scope.plot_time_series(('S_NH', 'S_S'))"
]
},
{
"cell_type": "markdown",
"id": "e11-tracker-note",
"metadata": {},
"source": [
"Every `SanUnit` and `WasteStream` has a `.scope` attribute, but only the ones in `sys.scope.subjects` (shown above) actually accumulate data during a simulation. For everything else, the scope object exists but its `record` stays empty.\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "51cc75b4",
"metadata": {
"execution": {
"iopub.execute_input": "2026-05-30T20:38:05.212884Z",
"iopub.status.busy": "2026-05-30T20:38:05.211885Z",
"iopub.status.idle": "2026-05-30T20:38:05.216330Z",
"shell.execute_reply": "2026-05-30T20:38:05.216330Z"
},
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Every SanUnit has a .scope attribute; here A2 is *not* in `sys.scope.subjects`\n",
"A2 = sys.flowsheet.unit.A2\n",
"A2.scope"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "e11-untracked-record",
"metadata": {
"execution": {
"iopub.execute_input": "2026-05-30T20:38:05.218334Z",
"iopub.status.busy": "2026-05-30T20:38:05.218334Z",
"iopub.status.idle": "2026-05-30T20:38:05.222615Z",
"shell.execute_reply": "2026-05-30T20:38:05.222615Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"array([], shape=(0, 1), dtype=float64)"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# ...but because A2 wasn't tracked, its record never got filled.\n",
"A2.scope.record"
]
},
{
"cell_type": "markdown",
"id": "8b8727df",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"For `A1`, it is tracked, so each row in the `record` attribute is values of `A1`'s state variables at a certain time point."
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "cab34aab",
"metadata": {
"execution": {
"iopub.execute_input": "2026-05-30T20:38:05.224621Z",
"iopub.status.busy": "2026-05-30T20:38:05.224621Z",
"iopub.status.idle": "2026-05-30T20:38:05.230748Z",
"shell.execute_reply": "2026-05-30T20:38:05.229743Z"
},
"scrolled": true,
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"array([0.000e+00, 5.098e-10, 1.020e-09, 6.117e-09, 1.122e-08, 6.219e-08,\n",
" 1.132e-07, 3.166e-07, 5.199e-07, 7.233e-07, 1.403e-06, 2.083e-06,\n",
" 2.763e-06, 8.673e-06, 1.458e-05, 2.049e-05, 3.168e-05, 4.287e-05,\n",
" 5.406e-05, 6.525e-05, 1.049e-04, 1.446e-04, 1.843e-04, 2.239e-04,\n",
" 3.091e-04, 3.942e-04, 4.793e-04, 5.645e-04, 6.496e-04, 8.359e-04,\n",
" 1.022e-03, 1.209e-03, 1.395e-03, 1.581e-03, 1.768e-03, 2.185e-03,\n",
" 2.602e-03, 2.896e-03, 3.189e-03, 3.399e-03, 3.567e-03, 3.736e-03,\n",
" 3.905e-03, 4.038e-03, 4.171e-03, 4.304e-03, 4.438e-03, 4.571e-03,\n",
" 4.704e-03, 4.849e-03, 4.993e-03, 5.138e-03, 5.283e-03, 5.427e-03,\n",
" 5.572e-03, 5.833e-03, 6.093e-03, 6.354e-03, 6.614e-03, 6.875e-03,\n",
" 7.332e-03, 7.790e-03, 8.248e-03, 8.706e-03, 9.409e-03, 1.011e-02,\n",
" 1.081e-02, 1.152e-02, 1.274e-02, 1.396e-02, 1.518e-02, 1.641e-02,\n",
" 1.848e-02, 2.055e-02, 2.195e-02, 2.335e-02, 2.426e-02, 2.516e-02,\n",
" 2.607e-02, 2.698e-02, 2.948e-02, 3.073e-02, 3.199e-02, 3.324e-02,\n",
" 3.386e-02, 3.449e-02, 3.511e-02, 3.730e-02, 3.949e-02, 4.168e-02,\n",
" 4.688e-02, 5.208e-02, 5.304e-02, 5.378e-02, 5.453e-02, 5.527e-02,\n",
" 5.601e-02, 5.719e-02, 5.836e-02, 5.954e-02, 6.201e-02, 6.447e-02,\n",
" 6.694e-02, 6.940e-02, 7.411e-02, 7.883e-02, 7.942e-02, 8.001e-02,\n",
" 8.060e-02, 8.126e-02, 8.193e-02, 8.259e-02, 8.325e-02, 8.392e-02,\n",
" 8.458e-02, 8.524e-02, 8.560e-02, 8.596e-02, 8.631e-02, 8.674e-02,\n",
" 8.717e-02, 8.763e-02, 8.809e-02, 9.040e-02, 9.270e-02, 9.500e-02,\n",
" 9.584e-02, 9.667e-02, 9.751e-02, 1.040e-01, 1.105e-01, 1.111e-01,\n",
" 1.116e-01, 1.122e-01, 1.175e-01, 1.229e-01, 1.239e-01, 1.248e-01,\n",
" 1.258e-01, 1.264e-01, 1.270e-01, 1.273e-01, 1.277e-01, 1.282e-01,\n",
" 1.286e-01, 1.292e-01, 1.298e-01, 1.304e-01, 1.333e-01, 1.362e-01,\n",
" 1.370e-01, 1.377e-01, 1.384e-01, 1.440e-01, 1.447e-01, 1.453e-01,\n",
" 1.471e-01, 1.488e-01, 1.606e-01, 1.724e-01, 1.738e-01, 1.753e-01,\n",
" 1.767e-01, 1.913e-01, 2.058e-01, 2.415e-01, 2.771e-01, 3.127e-01,\n",
" 3.741e-01, 4.356e-01, 4.970e-01, 5.584e-01, 6.171e-01, 6.757e-01,\n",
" 7.343e-01, 7.930e-01, 9.226e-01, 1.052e+00, 1.182e+00, 1.311e+00,\n",
" 1.498e+00, 1.684e+00, 1.871e+00, 2.241e+00, 2.612e+00, 2.983e+00,\n",
" 3.353e+00, 3.893e+00, 4.433e+00, 4.972e+00, 5.512e+00, 6.349e+00,\n",
" 7.186e+00, 8.023e+00, 8.860e+00, 1.042e+01, 1.198e+01, 1.354e+01,\n",
" 1.510e+01, 1.740e+01, 1.971e+01, 2.201e+01, 2.431e+01, 2.829e+01,\n",
" 3.227e+01, 3.625e+01, 4.022e+01, 4.682e+01, 5.000e+01])"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# `time_series` stores the time data\n",
"A1.scope.time_series"
]
},
{
"cell_type": "markdown",
"id": "c14d81b0",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"The tracked time-series data can be exported to a file in two ways.\n",
"```python\n",
"sys.scope.export('bsm1_time_series.xlsx')\n",
"```\n",
"\n",
"or\n",
"\n",
"```python\n",
"import numpy as np\n",
"sys.simulate(state_reset_hook='reset_cache',\n",
" t_span=(0, 50),\n",
" t_eval=np.arange(0, 51, 1),\n",
" method='BDF',\n",
" export_state_to=('bsm1_time_series.xlsx'))\n",
"```"
]
},
{
"cell_type": "markdown",
"id": "5b93411d",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"We can also (re-)define which unit or stream to track after the system has been constructed."
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "b818bfbf",
"metadata": {
"execution": {
"iopub.execute_input": "2026-05-30T20:38:05.231756Z",
"iopub.status.busy": "2026-05-30T20:38:05.231756Z",
"iopub.status.idle": "2026-05-30T20:38:05.237184Z",
"shell.execute_reply": "2026-05-30T20:38:05.237184Z"
},
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(, )"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Let's say we want to track the clarifier and the waste activated sludge\n",
"C1 = sys.flowsheet.unit.C1\n",
"WAS = sys.flowsheet.stream.WAS\n",
"sys.set_dynamic_tracker(C1, WAS)\n",
"sys.scope.subjects"
]
},
{
"cell_type": "markdown",
"id": "f5386dbd",
"metadata": {},
"source": [
"However, we would need to rerun the simulation to retrieve results."
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "f6b35327",
"metadata": {
"execution": {
"iopub.execute_input": "2026-05-30T20:38:05.239196Z",
"iopub.status.busy": "2026-05-30T20:38:05.239196Z",
"iopub.status.idle": "2026-05-30T20:38:06.920099Z",
"shell.execute_reply": "2026-05-30T20:38:06.920099Z"
},
"scrolled": false,
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"data": {
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"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# You can use a shorter time or try changing method to 'RK23' (explicit solver) if it takes a long time\n",
"sys.simulate(t_span=(0, 50), method='BDF', state_reset_hook='reset_cache')\n",
"# The clarifier are modeled as 10 layers, so we can track the TSS in each layer\n",
"fig, ax = C1.scope.plot_time_series([f'TSS{i}' for i in range(1,11)])"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "68dcbad5",
"metadata": {
"execution": {
"iopub.execute_input": "2026-05-30T20:38:06.924248Z",
"iopub.status.busy": "2026-05-30T20:38:06.924248Z",
"iopub.status.idle": "2026-05-30T20:38:07.111176Z",
"shell.execute_reply": "2026-05-30T20:38:07.111176Z"
},
"scrolled": false,
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = WAS.scope.plot_time_series(('X_BH', 'X_BA'))"
]
},
{
"cell_type": "markdown",
"id": "0eb92bf1",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"So far we've learned how to simulate any dynamic system developed with QSDsan. \n",
"A complete list of existing unit operations within QSDsan is available [in the documentation](https://qsdsan.readthedocs.io/en/latest/api/unit_operations/index.html). Unit operations labeled as \"QSDsan dynamic\" are enabled for dynamic simulations. Any system composed of the enabled units can be simulated dynamically as we learned above."
]
},
{
"cell_type": "markdown",
"id": "3d13e036",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### 1.2. When is a system \"dynamic\"?\n",
"It's ultimately the user's decision whether a system should be run dynamically. This section will cover the essentials to switch to the dynamic mode for system simulation."
]
},
{
"cell_type": "markdown",
"id": "94eab6a5",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"#### 1.2.1. `System.isdynamic` vs. `SanUnit.isdynamic` vs. `SanUnit.hasode` \n",
"\n",
"- Simply speaking, when `.isdynamic` is `True`, the program will attempt dynamic simulation. Users can directly enable/disable the dynamic mode by setting the `isdynamic` property of a `System` object.\n",
"\n",
"- The program will set the value of `.isdynamic` when it's not specified by users. `.isdynamic` is considered `True` in all cases except when `.isdynamic` is `False` for all units.\n",
"\n",
"- Setting `.isdynamic = True` does not guarantee the unit can be simulated dynamically. Just like how the `_run` method must be defined for static simulation, a series of additional methods must be defined to enable dynamic simulation.\n",
"\n",
"- If a unit operation has ODE algorithms, i.e., `.hasode` is `True`, it means a unit has the fundamental methods to compile ODEs. This is a **sufficient but not necessary** condition for dynamic simulation, because a unit doesn't have to be described with ODEs to be capable of dynamic simulations."
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "c130f36f",
"metadata": {
"execution": {
"iopub.execute_input": "2026-05-30T20:38:07.114062Z",
"iopub.status.busy": "2026-05-30T20:38:07.114062Z",
"iopub.status.idle": "2026-05-30T20:38:07.118259Z",
"shell.execute_reply": "2026-05-30T20:38:07.118259Z"
},
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"{: True,\n",
" : True,\n",
" : True,\n",
" : True,\n",
" : True,\n",
" : True}"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# All units in the BSM1 system above have ODEs\n",
"{u: u.hasode for u in sys.units}"
]
},
{
"cell_type": "markdown",
"id": "33a3d638",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## 2. Writing a dynamic `SanUnit` \n",
"\n",
"Whether a system can be simulated dynamically ultimately boils down to whether all the units in the system have the fundamental methods required for dynamic simulations. In this section, you'll learn how to implement your own algorithms to create a `SanUnit` subclass capable of dynamic simulations."
]
},
{
"cell_type": "markdown",
"id": "e11-sec-2-1-heading",
"metadata": {},
"source": [
"### 2.1. How the integrator drives a dynamic unit \n",
"\n",
"Before getting into the per-method mechanics in §2.2 and §2.3, it helps to picture **how** the integrator orchestrates a dynamic system over one step. The figure below shows one update cycle.\n",
"\n",
"\n",
"\n",
"\n",
"*Per step:* `solve_ivp` passes `(t, y)` down. Each unit's `_compile_AE` / `_compile_ODE` writes its `_state` and `_dstate`. `_update_state` / `_update_dstate` write the unit's state into each outlet `WasteStream`. The downstream unit reads its inlets' `.state` / `.dstate` as `y_ins` / `dy_ins`. The aggregated `dy/dt` (across the system's ODE state) is returned to `solve_ivp`, which advances time.\n",
"\n",
"A few things to note from this picture:\n",
"\n",
"- **State lives in two places.** Each `SanUnit` keeps its own `_state` / `_dstate` arrays (what the unit's algorithm is computing); each `WasteStream` keeps its `state` / `dstate` arrays (the inlets the next unit reads). The two are not the same buffer. `_update_state` and `_update_dstate` are the bridge: they're called inside the unit's compiled function so the outlet stream sees the latest values.\n",
"- **Units communicate only through streams.** A downstream unit never reads its upstream's `_state` directly — it reads its inlets' `state` (and `dstate`) as `y_ins` (and `dy_ins`).\n",
"- **One global state vector.** The integrator's `y` vector is the concatenation of the ODE-state of every unit. `solve_ivp` doesn't know about streams; it just sees one big state vector."
]
},
{
"cell_type": "markdown",
"id": "e11-sec-ae-vs-ode",
"metadata": {},
"source": [
"#### 2.1.1. `_compile_AE` vs. `_compile_ODE`: which one when?\n",
"\n",
"`_compile_AE` (for **algebraic equation** units) and `_compile_ODE` (for **ordinary differential equation** units) both fit the same data-flow diagram, but they describe different physics.\n",
"\n",
"- `_compile_ODE` — use when the unit has *holdup* or *inertia*: tanks, reactors, settlers, anywhere a balance reads `d(state)/dt = inflow − outflow + reactions`. The function you compile populates `_dstate`; the integrator integrates it.\n",
"- `_compile_AE` — use when the unit's state is *instantaneously* determined by its inputs: mixers, splitters, pumps without holdup, hydraulic delays. There is no time derivative to integrate; the function you compile sets `_state` directly each time the integrator asks for the system's state.\n",
"\n",
"Mixed systems work naturally: AE units act as **pass-throughs within each integrator step**, while ODE units are what `solve_ivp` actually integrates. If you find yourself writing `dy_dt = 0` for everything in a unit, you probably wanted `_compile_AE` instead.\n",
"\n",
"
\n",
"\n",
"**Tip:** A unit declares its choice by implementing the corresponding `_compile_*` method and exposing the matching `AE` or `ODE` property (you'll see both patterns in §2.4 and §2.5). A unit can implement only one of the two: an ODE unit doesn't need an AE; an AE unit doesn't need an ODE.\n",
"\n",
"
"
]
},
{
"cell_type": "markdown",
"id": "220c984a",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### 2.2. Basic structure\n",
"\n",
"During **static** system simulations, `_run` directly defines the mass and/or energy flows of the effluent `WasteStream` objects of the unit after calculation. \n",
"\n",
"In comparison, during **dynamic** simulations, all information are stored as `_state` and `_dstate` attributes of the relevant `SanUnit` objects as well as `state` and `dstate` properties of `WasteStream` objects. These information won't be translated to mass or energy flows until dynamic simulation is completed.\n",
"\n",
"- `WasteStream.state` is a 1d `numpy.array` of length $n+1$, $n$ is the length of the components associated with the `thermo`. Each element of the array represents value of one state variable."
]
},
{
"cell_type": "markdown",
"id": "b1529db3",
"metadata": {
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"slide_type": "subslide"
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"source": [
"
\n",
"\n",
"**Tip:** When the dynamic simulation finishes, QSDsan reconstructs each `WasteStream`'s mass flow as `state[:-1] * state[-1]` (in g/d). That product determines what the two conventions below mean.\n",
"\n",
"- **Liquid `WasteStream` (the default):** the first $n$ elements are component concentrations \\[mg/L = g/m³\\] and the last element is the total volumetric flow \\[m³/d\\]. Their product gives mass flow \\[g/d\\].\n",
"- **Gaseous `WasteStream`:** a gas's volumetric flow at the operating $T$ and $P$ depends on composition, so working in concentrations is awkward. As the same `state[:-1] * state[-1]` algorithm still has to produce mass flow, so the convention is to store the component mass flows \\[g/d\\] directly in the first $n$ elements and fix the last element at $1$, so the reconstructed mass flow is whatever you stored.\n",
"\n",
"
"
]
},
{
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"id": "d997b05d",
"metadata": {
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"source": [
"- `WasteStream.dstate` is an array of the exact same shape as `WasteStream.state`, storing values of the time derivatives (i.e., the rates of change) of the state variables."
]
},
{
"cell_type": "code",
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"id": "825050c1",
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{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sf = sys.flowsheet.stream\n",
"sf.effluent.dstate.shape == sf.effluent.state.shape"
]
},
{
"cell_type": "markdown",
"id": "eb706d47",
"metadata": {
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"source": [
"`SanUnit._state` is also a 1d `numpy.array`, but the length of the array is not assumed, because the state variables relevant for a `SanUnit` is entirely dependent on the unit operation itself. Therefore, there is no predefined units of measure or order for state variables of a unit operation."
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "956dbc0f",
"metadata": {
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"False"
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"execution_count": 20,
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],
"source": [
"C1._state.shape == A1._state.shape\n",
"# C1._state.shape == C1._dstate.shape"
]
},
{
"cell_type": "markdown",
"id": "38e2a8f8",
"metadata": {},
"source": [
"But similar to `WasteStream`, `SanUnit._dstate` must have the exact same shape as the `_state` array, as each element corresponds to the time derivative of a state variable."
]
},
{
"cell_type": "code",
"execution_count": 21,
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"outputs": [
{
"data": {
"text/plain": [
"{'S_I': 30.0,\n",
" 'S_S': 2.8098492768502354,\n",
" 'X_I': 1147.9023456598527,\n",
" 'X_S': 82.1496729433189,\n",
" 'X_BH': 2551.149645860236,\n",
" 'X_BA': 148.1855399395735,\n",
" 'X_P': 447.1138645933302,\n",
" 'S_O': 0.004288922586191978,\n",
" 'S_NO': 5.338947439089076,\n",
" 'S_NH': 7.9289428538204,\n",
" 'S_ND': 1.216685860427014,\n",
" 'X_ND': 5.285736721431849,\n",
" 'S_ALK': 59.15831466857708,\n",
" 'S_N2': 25.00787300530831,\n",
" 'H2O': 997375.2641947934,\n",
" 'Q': 92230.0}"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Some dynamic units in QSDsan have a `state` property that formats\n",
"# the data in `_state` for better readability\n",
"A1.state"
]
},
{
"cell_type": "markdown",
"id": "b6a928f2",
"metadata": {
"slideshow": {
"slide_type": "slide"
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},
"source": [
"### 2.3. Fundamental methods\n",
"In addition to proper `__init__` and `_run` methods ([SanUnit advanced tutorial](https://qsdsan.readthedocs.io/en/latest/tutorials/5_SanUnit_advanced.html#1.1.-Fundamental-methods)), a few more methods are required in a `SanUnit` subclass for dynamic simulation. Users typically won't interact with these methods but they will be called by `System.simulate` to manipulate the values of the arrays mentioned above (i.e., `._state`, `._dstate`, `.state`, and `.dstate`)."
]
},
{
"cell_type": "markdown",
"id": "976dabeb",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"- `_init_state`, called after `_run` to generate an initial condition for the unit, i.e., defining shape and values of the `_state` and `_dstate` arrays. For example:\n",
"```python\n",
"import numpy as np\n",
"def _init_state(self):\n",
" inf = self.ins[0]\n",
" self._state = np.ones(len(inf.components)+1)\n",
" self._dstate = self._state * 0.\n",
"```\n",
"This method (not saying it makes sense) assumes $n+1$ state variables and gives an initial value of 1 to all of them. Then it also sets the initial time derivatives to be 0. "
]
},
{
"cell_type": "markdown",
"id": "3a3de71d",
"metadata": {
"slideshow": {
"slide_type": "subslide"
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"source": [
"- `_update_state`, to update effluent streams' state arrays based on current state (and maybe dstate) of the SanUnit. For example:\n",
"```python\n",
"def _update_state(self):\n",
" arr = self._state # retrieving the current state of the SanUnit\n",
" eff, = self.outs # assuming this SanUnit has one outlet only\n",
" eff.state[:] = arr # assume arr has the same shape as WasteStream.state\n",
"```\n",
"The goal of this method is to update the values in `.state` for each `WasteStream` in `.outs`."
]
},
{
"cell_type": "markdown",
"id": "8deec2a2",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"- `_update_dstate`, to update effluent streams' `dstate` arrays based on current `_state` and `_dstate` of the SanUnit. The signature and often the algorithm are similar to `_update_state`.\n",
"\n",
"\n",
"- `_compile_ODE` or `_compile_AE`, used to define the function that updates the `_dstate` and/or `_state` of the `SanUnit` based on its influent streams' `state`/`dstate` and potentially its own current state. The defined function will be stored as `SanUnit._ODE` or `SanUnit._AE`. These methods should follow some general forms like below:\n",
"```python\n",
"@property\n",
"def ODE(self):\n",
" if self._ODE is None:\n",
" self._compile_ODE()\n",
" return self._ODE \n",
"```"
]
},
{
"cell_type": "markdown",
"id": "a431142f",
"metadata": {
"slideshow": {
"slide_type": "subslide"
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},
"source": [
"```python\n",
"def _compile_ODE(self):\n",
" _dstate = self._dstate\n",
" _update_dstate = self._update_dstate\n",
" def dy_dt(t, y_ins, y, dy_ins):\n",
" _dstate[:] = some_algorithm(t, y_ins, y, dy_ins)\n",
" _update_dstate()\n",
" self._ODE = dy_dt\n",
"```"
]
},
{
"cell_type": "markdown",
"id": "83c50a89",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"```python\n",
"@property\n",
"def AE(self):\n",
" if self._AE is None:\n",
" self._compile_AE()\n",
" return self._AE\n",
"```"
]
},
{
"cell_type": "markdown",
"id": "dd66c263",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"```python\n",
"def _compile_AE(self):\n",
" _state = self._state\n",
" _dstate = self._dstate\n",
" _update_state = self._update_state\n",
" _update_dstate = self._update_dstate\n",
" def y_t(t, y_ins, dy_ins):\n",
" _state[:] = some_algorithm(t, y_ins, dy_ins)\n",
" _dstate[:] = some_other_algorithm(t, y_ins, dy_ins)\n",
" _update_state()\n",
" _update_dstate()\n",
" self._AE = y_t\n",
"```"
]
},
{
"cell_type": "markdown",
"id": "a144502d",
"metadata": {
"slideshow": {
"slide_type": "subslide"
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},
"source": [
"
\n",
"\n",
"**Note:** When writing the `dy_dt` or `y_t` functions, use `._state[:] = ` rather than `._state = ` because it's generally faster to update values in an existing array than overwriting this array with a newly created array.\n",
"\n",
"
\n",
"\n",
"In the next subsection, we'll learn more about the `ODE` and `AE` methods."
]
},
{
"cell_type": "markdown",
"id": "afd475f2",
"metadata": {
"slideshow": {
"slide_type": "slide"
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},
"source": [
"### 2.4. Making a simple MixerSplitter (`_compile_AE`)\n",
"\n",
"Let's say we want to make an ideal mixer-splitter that instantly mixes all streams at the inlets and then evenly split them across the outlets."
]
},
{
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"source": [
"# Typically if implemented as a static SanUnit, it'd be pretty simple\n",
"# Let's ignore `_design` and `_cost` for now.\n",
"class MixerSplitter1(qs.SanUnit):\n",
" _N_outs = 3\n",
" _ins_size_is_fixed = False\n",
" _outs_size_is_fixed = False\n",
" def __init__(self, ID='', ins=None, outs=(), thermo=None, \n",
" init_with='WasteStream', **kwargs):\n",
" qs.SanUnit.__init__(self, ID, ins, outs, thermo, init_with, **kwargs)\n",
" self.mixed = qs.WasteStream()\n",
" \n",
" def _run(self):\n",
" mixed = self.mixed\n",
" mixed.mix_from(self.ins)\n",
" n_outs = len(self.outs)\n",
" flow = mixed.get_total_flow('kg/hr')/n_outs\n",
" for out in self.outs:\n",
" out.copy_like(mixed)\n",
" out.set_total_flow(flow, 'kg/hr')\n",
" \n",
" def _design(self):\n",
" pass\n",
" \n",
" def _cost(self):\n",
" pass"
]
},
{
"cell_type": "code",
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"id": "9b5ce52d",
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CompiledComponents([\n",
" S_I, S_S, X_I, X_S, \n",
" X_BH, X_BA, X_P, S_O, \n",
" S_NO, S_NH, S_ND, X_ND,\n",
" S_ALK, S_N2, H2O, \n",
"])\n"
]
}
],
"source": [
"# Let's try simulating it with the components used in BSM1\n",
"cmps = qs.get_thermo().chemicals\n",
"cmps.show()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "12aa03d9",
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},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"WasteStream: inf2\n",
"phase: 'l', T: 298.15 K, P: 101325 Pa\n",
"flow (g/hr): S_S 3e+03\n",
" S_NH 2.1e+03\n",
" H2O 8e+05\n",
" WasteStream-specific properties:\n",
" pH : 7.0\n",
" Alkalinity : 2.5 mmol/L\n",
" COD : 3708.6 mg/L\n",
" BOD : 2659.0 mg/L\n",
" TC : 1186.7 mg/L\n",
" TOC : 1186.7 mg/L\n",
" TN : 2596.0 mg/L\n",
" TP : 37.1 mg/L\n",
" Component concentrations (mg/L):\n",
" S_S 3708.6\n",
" S_NH 2596.0\n",
" H2O 988949.8\n"
]
}
],
"source": [
"# Now let's make a couple fake influents\n",
"inf1 = qs.WasteStream('inf1', H2O=1000, S_O=5)\n",
"inf2 = qs.WasteStream('inf2', H2O=800, S_S=3, S_NH=2.1)\n",
"inf2.show()"
]
},
{
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"MixerSplitter1: M1\n",
"ins...\n",
"[0] inf1\n",
"phase: 'l', T: 298.15 K, P: 101325 Pa\n",
"flow (g/hr): S_O 5e+03\n",
" H2O 1e+06\n",
" WasteStream-specific properties:\n",
" pH : 7.0\n",
" Alkalinity : 2.5 mmol/L\n",
"[1] inf2\n",
"phase: 'l', T: 298.15 K, P: 101325 Pa\n",
"flow (g/hr): S_S 3e+03\n",
" S_NH 2.1e+03\n",
" H2O 8e+05\n",
" WasteStream-specific properties:\n",
" pH : 7.0\n",
" Alkalinity : 2.5 mmol/L\n",
" COD : 3708.6 mg/L\n",
" BOD : 2659.0 mg/L\n",
" TC : 1186.7 mg/L\n",
" TOC : 1186.7 mg/L\n",
" TN : 2596.0 mg/L\n",
" TP : 37.1 mg/L\n",
"outs...\n",
"[0] ws11\n",
"phase: 'l', T: 298.15 K, P: 101325 Pa\n",
"flow (g/hr): S_S 1e+03\n",
" S_O 1.67e+03\n",
" S_NH 700\n",
" H2O 6e+05\n",
" WasteStream-specific properties:\n",
" pH : 7.0\n",
" Alkalinity : 2.5 mmol/L\n",
" COD : 1650.2 mg/L\n",
" BOD : 1183.2 mg/L\n",
" TC : 528.1 mg/L\n",
" TOC : 528.1 mg/L\n",
" TN : 1155.1 mg/L\n",
" TP : 16.5 mg/L\n",
"[1] ws12\n",
"phase: 'l', T: 298.15 K, P: 101325 Pa\n",
"flow (g/hr): S_S 1e+03\n",
" S_O 1.67e+03\n",
" S_NH 700\n",
" H2O 6e+05\n",
" WasteStream-specific properties:\n",
" pH : 7.0\n",
" Alkalinity : 2.5 mmol/L\n",
" COD : 1650.2 mg/L\n",
" BOD : 1183.2 mg/L\n",
" TC : 528.1 mg/L\n",
" TOC : 528.1 mg/L\n",
" TN : 1155.1 mg/L\n",
" TP : 16.5 mg/L\n",
"[2] ws13\n",
"phase: 'l', T: 298.15 K, P: 101325 Pa\n",
"flow (g/hr): S_S 1e+03\n",
" S_O 1.67e+03\n",
" S_NH 700\n",
" H2O 6e+05\n",
" WasteStream-specific properties:\n",
" pH : 7.0\n",
" Alkalinity : 2.5 mmol/L\n",
" COD : 1650.2 mg/L\n",
" BOD : 1183.2 mg/L\n",
" TC : 528.1 mg/L\n",
" TOC : 528.1 mg/L\n",
" TN : 1155.1 mg/L\n",
" TP : 16.5 mg/L\n"
]
}
],
"source": [
"MS1 = MixerSplitter1(ins=(inf1, inf2))\n",
"MS1.simulate()\n",
"MS1.show()"
]
},
{
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"id": "72151a1e",
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"tags": [
"raises-exception"
]
},
"outputs": [
{
"ename": "AttributeError",
"evalue": "'MixerSplitter1' object has no attribute '_init_state'",
"output_type": "error",
"traceback": [
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
"\u001b[31mAttributeError\u001b[39m Traceback (most recent call last)",
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[26]\u001b[39m\u001b[32m, line 5\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;66;03m# Obviously, it's not ready for dynamic simulation\u001b[39;00m\n\u001b[32m 2\u001b[39m \u001b[38;5;66;03m# You will receive an error if you try to simulate it with `isdynamic=True`\u001b[39;00m\n\u001b[32m 3\u001b[39m MS1_dyn = MixerSplitter1(ins=(inf1.copy(), inf2.copy()), isdynamic=\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[32m 4\u001b[39m dyn_sys = qs.System(path=(MS1_dyn,))\n\u001b[32m----> \u001b[39m\u001b[32m5\u001b[39m dyn_sys.simulate(t_span=(\u001b[32m0\u001b[39m,\u001b[32m5\u001b[39m))\n",
"\u001b[36mFile \u001b[39m\u001b[32m~\\Documents\\Coding\\QSDsan-platform\\.venv\\Lib\\site-packages\\biosteam\\_system.py:3374\u001b[39m, in \u001b[36mSystem.simulate\u001b[39m\u001b[34m(self, update_configuration, units, design_and_cost, **kwargs)\u001b[39m\n\u001b[32m 3354\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34msimulate\u001b[39m(\u001b[38;5;28mself\u001b[39m, update_configuration: Optional[\u001b[38;5;28mbool\u001b[39m]=\u001b[38;5;28;01mNone\u001b[39;00m, units=\u001b[38;5;28;01mNone\u001b[39;00m, \n\u001b[32m 3355\u001b[39m design_and_cost=\u001b[38;5;28;01mNone\u001b[39;00m, **kwargs):\n\u001b[32m 3356\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 3357\u001b[39m \u001b[33;03m If system is dynamic, run the system dynamically. Otherwise, converge \u001b[39;00m\n\u001b[32m 3358\u001b[39m \u001b[33;03m the path of unit operations to steady state. After running/converging \u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 3372\u001b[39m \u001b[33;03m \u001b[39;00m\n\u001b[32m 3373\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m3374\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m.flowsheet:\n\u001b[32m 3375\u001b[39m specifications = \u001b[38;5;28mself\u001b[39m._specifications\n\u001b[32m 3376\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m specifications \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m._running_specifications:\n",
"\u001b[36mFile \u001b[39m\u001b[32m~\\Documents\\Coding\\QSDsan-platform\\.venv\\Lib\\site-packages\\biosteam\\_flowsheet.py:120\u001b[39m, in \u001b[36mFlowsheet.__exit__\u001b[39m\u001b[34m(self, type, exception, traceback)\u001b[39m\n\u001b[32m 118\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m__exit__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28mtype\u001b[39m, exception, traceback):\n\u001b[32m 119\u001b[39m main_flowsheet.set_flowsheet(\u001b[38;5;28mself\u001b[39m._temporary_stack.pop())\n\u001b[32m--> \u001b[39m\u001b[32m120\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m exception: \u001b[38;5;28;01mraise\u001b[39;00m exception\n",
"\u001b[36mFile \u001b[39m\u001b[32m~\\Documents\\Coding\\QSDsan-platform\\.venv\\Lib\\site-packages\\biosteam\\_system.py:3418\u001b[39m, in \u001b[36mSystem.simulate\u001b[39m\u001b[34m(self, update_configuration, units, design_and_cost, **kwargs)\u001b[39m\n\u001b[32m 3416\u001b[39m \u001b[38;5;28mself\u001b[39m._setup(update_configuration, units)\n\u001b[32m 3417\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.isdynamic: \n\u001b[32m-> \u001b[39m\u001b[32m3418\u001b[39m outputs = \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mdynamic_run\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mkwargs\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 3419\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m design_and_cost: \u001b[38;5;28mself\u001b[39m._summary()\n\u001b[32m 3420\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n",
"\u001b[36mFile \u001b[39m\u001b[32m~\\Documents\\Coding\\QSDsan-platform\\.venv\\Lib\\site-packages\\biosteam\\_system.py:3509\u001b[39m, in \u001b[36mSystem.dynamic_run\u001b[39m\u001b[34m(self, **dynsim_kwargs)\u001b[39m\n\u001b[32m 3507\u001b[39m \u001b[38;5;66;03m# Load initial states\u001b[39;00m\n\u001b[32m 3508\u001b[39m \u001b[38;5;28mself\u001b[39m.converge()\n\u001b[32m-> \u001b[39m\u001b[32m3509\u001b[39m y0, idx, nr = \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43m_load_state\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 3510\u001b[39m dk[\u001b[33m'\u001b[39m\u001b[33my0\u001b[39m\u001b[33m'\u001b[39m] = y0\n\u001b[32m 3511\u001b[39m \u001b[38;5;66;03m# Integrate\u001b[39;00m\n",
"\u001b[36mFile \u001b[39m\u001b[32m~\\Documents\\Coding\\QSDsan-platform\\.venv\\Lib\\site-packages\\biosteam\\_system.py:3206\u001b[39m, in \u001b[36mSystem._load_state\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 3204\u001b[39m idx = {}\n\u001b[32m 3205\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m unit \u001b[38;5;129;01min\u001b[39;00m units: \n\u001b[32m-> \u001b[39m\u001b[32m3206\u001b[39m \u001b[30;43munit\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43m_init_state\u001b[39;49m()\n\u001b[32m 3207\u001b[39m unit._update_state()\n\u001b[32m 3208\u001b[39m unit._update_dstate()\n",
"\u001b[31mAttributeError\u001b[39m: 'MixerSplitter1' object has no attribute '_init_state'"
]
}
],
"source": [
"# Obviously, it's not ready for dynamic simulation\n",
"# You will receive an error if you try to simulate it with `isdynamic=True`\n",
"MS1_dyn = MixerSplitter1(ins=(inf1.copy(), inf2.copy()), isdynamic=True)\n",
"dyn_sys = qs.System(path=(MS1_dyn,))\n",
"dyn_sys.simulate(t_span=(0,5))"
]
},
{
"cell_type": "markdown",
"id": "0c4eb0cd",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Since the mixer-splitter mixes and splits instantly, we can express this process with a set of algebraic equations (AEs). Assume its array of state variables follow the \"concentration-volumetric flow\" convention. In mathematical forms, state variables of the mixer-splitter ($C_m$, component concentrations; $Q_m$, total volumetric flow) follow:\n",
"$$Q_m = \\sum_{i \\in ins} Q_i \\tag{1}$$\n",
"$$Q_mC_m = \\sum_{i \\in ins} Q_iC_i$$\n",
"$$\\therefore C_m = \\frac{\\sum_{i \\in ins} Q_iC_i}{Q_m} \\tag{2}$$"
]
},
{
"cell_type": "markdown",
"id": "a37f98d9",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"Therefore, the time derivatives $\\dot{Q_m}$ follow:\n",
"$$\\dot{Q_m} = \\sum_{i \\in ins} \\dot{Q_i} \\tag{3}$$\n",
"$$Q_m\\dot{C_m} + C_m\\dot{Q_m} = \\sum_{i \\in ins} (Q_i\\dot{C_i} + C_i\\dot{Q_i})$$\n",
"$$\\therefore \\dot{C_m} = \\frac{1}{Q_m}\\cdot(\\sum_{i \\in ins}Q_i\\dot{C_i} + \\sum_{i \\in ins}C_i\\dot{Q_i} - C_m\\dot{Q_m}) \\tag{4}$$"
]
},
{
"cell_type": "markdown",
"id": "7578a12e",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"For any effluent `WasteStream` $j$:\n",
"$$Q_j = \\frac{Q_m}{n_{outs}} \\tag{5}$$\n",
"$$C_j = C_m \\tag{6}$$\n",
"$$\\therefore \\dot{Q_j} = \\frac{\\dot{Q_m}}{n_{outs}} \\tag{7}$$\n",
"$$\\dot{C_j} = \\dot{C_m} \\tag{8}$$"
]
},
{
"cell_type": "markdown",
"id": "58304881",
"metadata": {},
"source": [
"The diagram below uses an illustrative case with $m = 3$ inlets and $n = 4$ components; the same shapes generalize to any `m` and `n`. Equation (1) is `Q_m = sum(Q_ins)`, equation (2) is the flow-weighted concentration `C_m = Q_ins · C_ins / Q_m`, and equations (5)–(8) just slice the result across the outlets.\n",
"\n",
"The matrix product `Q_ins · C_ins` yields an n-vector of flow-weighted sums, which divided by `Q_m` gives the mixer's component concentrations."
]
},
{
"cell_type": "markdown",
"id": "e11-fig-mixer-arrays",
"metadata": {},
"source": [
"\n",
""
]
},
{
"cell_type": "markdown",
"id": "d023b665",
"metadata": {},
"source": [
"Now, let's try to implement this algorithm in methods for dynamic simulation."
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "38abf7cb",
"metadata": {
"execution": {
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"slide_type": "slide"
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"outputs": [],
"source": [
"import numpy as np\n",
"class MixerSplitter2(MixerSplitter1):\n",
" def _init_state(self):\n",
" mixed = self.mixed\n",
" self._state = np.empty(len(cmps)+1)\n",
" self._state[:-1] = mixed.conc # first n element be the component concentrations of the mixed stream\n",
" self._state[-1] = mixed.F_vol * 24 # last element be the total volumetric flow, in m3/d\n",
" self._dstate = self._state * 0.\n",
" \n",
" def _update_state(self):\n",
" y = self._state\n",
" n_outs = len(self.outs)\n",
" for ws in self.outs:\n",
" if ws.state is None: ws.state = y.copy() # initialize the state using a copy\n",
" else: ws.state[:-1] = y[:-1] # equation (6)\n",
" ws.state[-1] = y[-1]/n_outs # equation (5)\n",
" \n",
" def _update_dstate(self):\n",
" dy = self._dstate\n",
" n_outs = len(self.outs)\n",
" for ws in self.outs:\n",
" if ws.dstate is None: ws.dstate = dy.copy()\n",
" else: ws.dstate[:-1] = dy[:-1] # equation (8)\n",
" ws.dstate[-1] = dy[-1]/n_outs # equation (7)\n",
" \n",
" @property\n",
" def AE(self):\n",
" if self._AE is None:\n",
" self._compile_AE()\n",
" return self._AE\n",
" \n",
" def _compile_AE(self):\n",
" _state = self._state\n",
" _dstate = self._dstate\n",
" _update_state = self._update_state\n",
" _update_dstate = self._update_dstate\n",
" def y_t(t, y_ins, dy_ins):\n",
" Q_ins = y_ins[:,-1]\n",
" C_ins = y_ins[:,:-1]\n",
" dQ_ins = dy_ins[:,-1]\n",
" dC_ins = dy_ins[:,:-1]\n",
" _state[-1] = Q = sum(Q_ins) # equation (1)\n",
" _state[:-1] = C = Q_ins @ C_ins / Q # equation (2)\n",
" _dstate[-1] = dQ = sum(dQ_ins) # equation (3)\n",
" _dstate[:-1] = dC = (Q_ins @ dC_ins + dQ_ins @ C_ins - C*dQ) / Q # equation (4)\n",
" _update_state()\n",
" _update_dstate()\n",
" self._AE = y_t"
]
},
{
"cell_type": "markdown",
"id": "da258438",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
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"source": [
"
\n",
"\n",
"**Note:** 1. All `SanUnit._AE` must take exactly these three positional arguments (`t`, `y_ins`, `dy_ins`). `t` is time as a `float`. Both `y_ins` and `dy_ins` are **2d** `numpy.array` of the same shape `(m, n+1)`, where $m$ is the number of inlets, $n+1$ is the length of the `state` or `dstate` array of a `WasteStream`.\n",
"\n",
"2. All `SanUnit._AE` must update both `_state` and `_dstate` of the `SanUnit`, and must call `_update_state` and `_update_dstate` afterwards.\n",
"\n",
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# You'll see the mass flows stay constant through the simulation period, \n",
"# but still it means the system was simulated dynamically.\n",
"fig, ax = MS2.scope.plot_time_series(('S_S', 'S_NH', 'S_O'))"
]
},
{
"cell_type": "markdown",
"id": "22788b98",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Many commonly used unit operations, such as [Pump](https://qsdsan.readthedocs.io/en/latest/api/unit_operations/bst/pumping.html#qsdsan.unit_operations.Pump), [Mixer](https://qsdsan.readthedocs.io/en/latest/api/unit_operations/bst/abstract.html#qsdsan.unit_operations.Mixer), [Splitter](https://qsdsan.readthedocs.io/en/latest/api/unit_operations/bst/abstract.html#qsdsan.unit_operations.Splitter), and [HydraulicDelay](https://qsdsan.readthedocs.io/en/latest/api/unit_operations/dynamic/abstract.html#qsdsan.unit_operations.HydraulicDelay), have implemented the fundamental methods to be used in a dynamic system. You can always refer to the source codes of these units to learn more about how they work."
]
},
{
"cell_type": "markdown",
"id": "a04a8ab5",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### 2.5. Making an inactive CompleteMixTank (`_compile_ODE`)"
]
},
{
"cell_type": "markdown",
"id": "21dca6ff",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"As you can see above, it's not very useful to dynamically simulate a system without any ODEs. So let's make a simple inactive complete mix tank (inactive means no reactions). Assume the reactor has a fixed liquid volume $V$, and thus the effluent volumetric flow rate changes instantly with influents. The mass balance of this type of reactor can be described as:\n",
"$$Q = \\sum_{i \\in ins} Q_i \\tag{9}$$\n",
"$$\\therefore \\dot{Q} = \\sum_{i \\in ins} \\dot{Q_i} \\tag{10}$$\n",
"$$\\frac{d(VC)}{dt} = \\sum_{i \\in ins} Q_iC_i - QC$$\n",
"$$\\therefore \\dot{C} = \\frac{1}{V}(\\sum_{i \\in ins} Q_iC_i - QC) \\tag{11}$$\n",
"Equations (10) and (11) are the governing ODEs of this unit."
]
},
{
"cell_type": "markdown",
"id": "e11-fig-cmt-arrays",
"metadata": {},
"source": [
"\n",
"\n",
"\n",
"A fixed-volume CSTR with instant mixing. The unit's state is what's *in* the tank: an n-vector of concentrations `C` and a scalar volumetric flow `Q`. The integrator advances both via the ODE shown.\n",
"\n",
"Notice the asymmetry with the MixerSplitter: there, state was algebraically determined by the inlets at each step. Here, state has inertia (the holdup `V`), so it needs a real `dy/dt`."
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "c4706ed2",
"metadata": {
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"slide_type": "slide"
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},
"outputs": [],
"source": [
"class CompleteMixTank(qs.SanUnit):\n",
" \n",
" _N_outs = 1\n",
" _ins_size_is_fixed = False\n",
" \n",
" def __init__(self, ID='', ins=None, outs=(), thermo=None, \n",
" init_with='WasteStream', V=10, **kwargs):\n",
" qs.SanUnit.__init__(self, ID, ins, outs, thermo, init_with, **kwargs)\n",
" self.V = V\n",
" \n",
" def _run(self):\n",
" out, = self.outs\n",
" out.mix_from(self.ins)\n",
" \n",
" def set_init_conc(self,**concentrations):\n",
" cmps = self.thermo.chemicals\n",
" C = np.zeros(len(cmps))\n",
" idx = cmps.indices(list(concentrations.keys()))\n",
" C[idx] = list(concentrations.values())\n",
" self._init_concs = C\n",
" \n",
" def _init_state(self):\n",
" out, = self.outs\n",
" self._state = np.empty(len(cmps)+1)\n",
" self._state[:-1] = self._init_concs # first n element be the component concentrations of the mixed stream\n",
" self._state[-1] = out.F_vol*24 # last element be the total volumetric flow\n",
" self._dstate = self._state*0.\n",
" \n",
" def _update_state(self):\n",
" out, = self.outs\n",
" out.state = self._state\n",
" \n",
" def _update_dstate(self):\n",
" out, = self.outs\n",
" out.dstate = self._dstate\n",
" \n",
" @property\n",
" def ODE(self):\n",
" if self._ODE is None:\n",
" self._compile_ODE()\n",
" return self._ODE \n",
" \n",
" def _compile_ODE(self):\n",
" _dstate = self._dstate\n",
" _update_dstate = self._update_dstate\n",
" V = self.V\n",
" def dy_dt(t, y_ins, y, dy_ins):\n",
" Q_ins = y_ins[:,-1]\n",
" C_ins = y_ins[:,:-1]\n",
" dQ_ins = dy_ins[:,-1]\n",
" Q = sum(Q_ins) # equation (9)\n",
" C = y[:-1]\n",
" _dstate[-1] = sum(dQ_ins) # dQ, equation (10)\n",
" _dstate[:-1] = (Q_ins @ C_ins - Q*C)/V # dC, equation (11)\n",
" _update_dstate()\n",
" self._ODE = dy_dt"
]
},
{
"cell_type": "markdown",
"id": "472f1577",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"
\n",
"\n",
"**Note:** 1. All `SanUnit._ODE` must take exactly these four positional arguments: `t`, `y_ins`, and `dy_ins` are the same as the ones in `SanUnit._AE`. `y` is a **1d** `numpy.array`, because it is equal to the `_state` array of the unit.\n",
"\n",
"2. Unlike `_AE`, all `SanUnit._ODE` updates only the `_dstate` array of the `SanUnit`, and only calls `_update_dstate` afterwards.\n",
"\n",
"
"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "493239c1",
"metadata": {
"execution": {
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"outputs": [],
"source": [
"# Let's see if it works\n",
"CMT = CompleteMixTank(ins=(inf1.copy(), inf2.copy()), V=50,\n",
" isdynamic=True)\n",
"dyn_sys3 = qs.System(path=(CMT,))\n",
"dyn_sys3.set_dynamic_tracker(CMT)\n",
"\n",
"# We set the initial condition to be different from the steady state,\n",
"# so we can see how the system evolves to the steady state.\n",
"CMT.set_init_conc(S_S=500, S_NH=700, S_O=290)\n",
"dyn_sys3.simulate(t_span=(0,5))"
]
},
{
"cell_type": "code",
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"id": "c3df8f02",
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"execution": {
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"data": {
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"(
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],
"source": [
"CMT.scope.plot_time_series(('S_NH', 'S_S', 'S_O'))"
]
},
{
"cell_type": "markdown",
"id": "3970aeaa",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Many commonly used unit operations described by ODEs have been implemented in QSDsan, such as [CSTR](https://qsdsan.readthedocs.io/en/latest/api/unit_operations/dynamic/suspended_growth_bioreactor.html#qsdsan.unit_operations.CSTR), [BatchExperiment](https://qsdsan.readthedocs.io/en/latest/api/unit_operations/dynamic/suspended_growth_bioreactor.html#qsdsan.unit_operations.BatchExperiment), and [FlatBottomCircularClarifier](https://qsdsan.readthedocs.io/en/latest/api/unit_operations/static/clarifier.html)."
]
},
{
"cell_type": "markdown",
"id": "b085b491",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## 3. Other convenient features \n",
"### 3.1. `ExogenousDynamicVariable`\n",
"The [ExogenousDynamicVariable](https://qsdsan.readthedocs.io/en/latest/api/utility_functions/dynamics.html#qsdsan.utils.ExogenousDynamicVariable) class is created to enable incorporation of exogenous dynamic variables in unit simulations. By \"dynamic\", it means the variable value changes over time. By \"exogenous\", it means the variable isn't explicitly dependent on any unit operation or stream. \"Ambient temperature\" or \"sunlight irradiance\" is a good example. They are environmental conditions that are often beyond control but have an effect on the operation or performance of the system."
]
},
{
"cell_type": "markdown",
"id": "d0365c64",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"There are generally two ways to create an `ExogenousDynamicVariable`:\n",
"\n",
"**1. Define the variable as a function of time.** Let's say we want to create a variable to represent the changing reaction temperature. Assume the temperature value \\[K\\] can be expressed as $T = 298.15 + 5\\cdot \\sin(t)$, indicating that the temperature fluctuates around $25^{\\circ}C$ by $\\pm 5^{\\circ}C$. Then simply,\n",
"```python\n",
"T = EDV('T', function=lambda t: 298.15+5*np.sin(t))\n",
"```"
]
},
{
"cell_type": "markdown",
"id": "e2885b32",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"**2. Provide time-series data to describe the dynamics of the variable.** For demonstration purpose, we'll just make up the data. In practice, this should be used if you have real data (e.g., knowing the change of temperature over time).\n",
"```python\n",
"t_arr = np.linspace(0, 5)\n",
"y_arr = 298.15+5*np.sin(t_arr)\n",
"T = EDV('T', t=t_arr, y=y_arr)\n",
"```"
]
},
{
"cell_type": "markdown",
"id": "8a5bd5b7",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Once created, these `ExogenousDynamicVariable` objects can be incorporated into any `SanUnit` upon its initialization or through the `SanUnit.exo_dynamic_vars` property setter. "
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "2639a8b7",
"metadata": {
"execution": {
"iopub.execute_input": "2026-05-30T20:38:08.242366Z",
"iopub.status.busy": "2026-05-30T20:38:08.241365Z",
"iopub.status.idle": "2026-05-30T20:38:10.078072Z",
"shell.execute_reply": "2026-05-30T20:38:10.078072Z"
},
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"All impact items have been removed from the registry.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Yalin\\Documents\\Coding\\QSDsan-platform\\.venv\\Lib\\site-packages\\thermosteam\\_stream.py:407: RuntimeWarning: has been replaced in registry\n",
" self._register(ID)\n"
]
},
{
"data": {
"text/plain": [
"(,)"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Let's see an example\n",
"from exposan.metab import create_system\n",
"sys_mt = create_system()\n",
"uf_mt = sys_mt.flowsheet.unit\n",
"uf_mt.R1.exo_dynamic_vars"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "a7e11837",
"metadata": {
"execution": {
"iopub.execute_input": "2026-05-30T20:38:10.080079Z",
"iopub.status.busy": "2026-05-30T20:38:10.080079Z",
"iopub.status.idle": "2026-05-30T20:38:10.083992Z",
"shell.execute_reply": "2026-05-30T20:38:10.083992Z"
},
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"[295.15]"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# The evaluation of these variables during unit simulation is done through \n",
"# the `eval_exo_dynamic_vars` method\n",
"uf_mt.R1.eval_exo_dynamic_vars(t=0.1)"
]
},
{
"cell_type": "markdown",
"id": "e11-edv-batch-intro",
"metadata": {},
"source": [
"#### 3.1.1. Batch construction from a file\n",
"\n",
"`EDV.batch_init` is the easiest way to define several variables at once. It expects a CSV/Excel file with a `t` column and one extra column per variable."
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "e11-edv-batch-code",
"metadata": {
"execution": {
"iopub.execute_input": "2026-05-30T20:38:10.085996Z",
"iopub.status.busy": "2026-05-30T20:38:10.085996Z",
"iopub.status.idle": "2026-05-30T20:38:10.109590Z",
"shell.execute_reply": "2026-05-30T20:38:10.109590Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"[, ]"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from qsdsan.utils import ExogenousDynamicVariable as EDV\n",
"import os, tempfile\n",
"import pandas as pd\n",
"\n",
"# Build a small time series in memory; in practice this would be a logged\n",
"# data file (e.g., ambient temperature and sunlight irradiance).\n",
"tmpdir = tempfile.mkdtemp()\n",
"csv_path = os.path.join(tmpdir, 'env_vars.csv')\n",
"t = np.linspace(0, 5, 51)\n",
"pd.DataFrame({\n",
" 't': t,\n",
" 'T': 293.15 + 5.0 * np.sin(2 * np.pi * t / 2.0), # 20 +/- 5 degC, 2-day period\n",
" 'I': 400.0 + 200.0 * np.sin(2 * np.pi * t / 1.0), # made-up irradiance, 1-day period\n",
"}).to_csv(csv_path, index=False)\n",
"\n",
"# `batch_init` returns one `ExogenousDynamicVariable` per non-`t` column.\n",
"edvs = EDV.batch_init(csv_path)\n",
"edvs\n"
]
},
{
"cell_type": "markdown",
"id": "e11-edv-use-intro",
"metadata": {},
"source": [
"#### 3.1.2. Using an EDV inside a custom dynamic `SanUnit`\n",
"\n",
"To plug an EDV into a `SanUnit`, pass it (or several) via `exogenous_vars=` at construction. Inside `_compile_ODE` or `_compile_AE`, call `self.eval_exo_dynamic_vars(t)` to read the current values. The example below extends the `CompleteMixTank` from §2.5 with a temperature-dependent first-order decay rate on one chosen component, using an Arrhenius form $k(T) = k_{ref}\\, \\exp\\!\\left(\\frac{E_a}{R}\\left(\\frac{1}{T_{ref}} - \\frac{1}{T}\\right)\\right)$."
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "e11-edv-class-code",
"metadata": {
"execution": {
"iopub.execute_input": "2026-05-30T20:38:10.112753Z",
"iopub.status.busy": "2026-05-30T20:38:10.112270Z",
"iopub.status.idle": "2026-05-30T20:38:10.118820Z",
"shell.execute_reply": "2026-05-30T20:38:10.117810Z"
}
},
"outputs": [],
"source": [
"class EnvAwareCMT(CompleteMixTank):\n",
" \"\"\"CompleteMixTank with a temperature-dependent first-order decay on one component.\"\"\"\n",
" def __init__(self, ID='', ins=None, outs=(), V=10, target='S_S',\n",
" k_ref=2.0, T_ref=293.15, Ea_R=4000.0,\n",
" exogenous_vars=(), **kwargs):\n",
" super().__init__(ID=ID, ins=ins, outs=outs, V=V,\n",
" exogenous_vars=exogenous_vars, **kwargs)\n",
" self._idx = self.thermo.chemicals.index(target)\n",
" self.k_ref, self.T_ref, self.Ea_R = k_ref, T_ref, Ea_R\n",
"\n",
" @property\n",
" def ODE(self):\n",
" if self._ODE is None: self._compile_ODE()\n",
" return self._ODE\n",
"\n",
" def _compile_ODE(self):\n",
" _dstate = self._dstate\n",
" _update_dstate = self._update_dstate\n",
" V, idx = self.V, self._idx\n",
" k_ref, T_ref, Ea_R = self.k_ref, self.T_ref, self.Ea_R\n",
" eval_exo = self.eval_exo_dynamic_vars\n",
" def dy_dt(t, y_ins, y, dy_ins):\n",
" Q_ins = y_ins[:, -1]\n",
" C_ins = y_ins[:, :-1]\n",
" Q = sum(Q_ins)\n",
" C = y[:-1]\n",
" _dstate[-1] = sum(dy_ins[:, -1])\n",
" _dstate[:-1] = (Q_ins @ C_ins - Q*C) / V # same as CompleteMixTank\n",
" T, = eval_exo(t) # exogenous T(t)\n",
" k = k_ref * np.exp(Ea_R * (1.0/T_ref - 1.0/T)) # Arrhenius\n",
" _dstate[idx] -= k * C[idx] # first-order sink\n",
" _update_dstate()\n",
" self._ODE = dy_dt\n"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "e11-edv-run-code",
"metadata": {
"execution": {
"iopub.execute_input": "2026-05-30T20:38:10.119824Z",
"iopub.status.busy": "2026-05-30T20:38:10.119824Z",
"iopub.status.idle": "2026-05-30T20:38:10.401324Z",
"shell.execute_reply": "2026-05-30T20:38:10.401324Z"
}
},
"outputs": [
{
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"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Reset thermo to bsm1.cmps (the METAB example switched it to ADM1).\n",
"qs.set_thermo(bsm1.cmps)\n",
"\n",
"# Pick the T variable from the batch we just loaded, build a clean inlet,\n",
"# and run the tank with V=50 m3 starting from S_S = 200 mg/L.\n",
"T_var = next(v for v in edvs if v.ID == 'T')\n",
"\n",
"inf_env = qs.WasteStream('inf_env', H2O=1000) # near-zero S_S inlet\n",
"tank = EnvAwareCMT(ID='T1', ins=inf_env, V=50,\n",
" target='S_S', k_ref=2.0, T_ref=293.15, Ea_R=4000.0,\n",
" exogenous_vars=(T_var,), isdynamic=True)\n",
"tank.set_init_conc(S_S=200.0)\n",
"sys_env = qs.System('env_sys', path=(tank,))\n",
"sys_env.set_dynamic_tracker(tank)\n",
"sys_env.simulate(t_span=(0, 5), method='BDF',\n",
" t_eval=np.linspace(0, 5, 201),\n",
" state_reset_hook='reset_cache')\n",
"\n",
"# Pull the tracked S_S series out of the scope record, then plot T(t) and\n",
"# S_S(t) on a shared figure so the decay-rate response to T is visible.\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# SanUnitScope.header entries look like ('T1', 'S_S [mg/L]'); split off the\n",
"# units suffix to match by component name.\n",
"S_S_idx = next(i for i, h in enumerate(tank.scope.header)\n",
" if h[1].split()[0] == 'S_S')\n",
"t_rec = tank.scope.time_series\n",
"S_S_rec = tank.scope.record[:, S_S_idx]\n",
"\n",
"t_grid = np.linspace(0, 5, 201)\n",
"T_grid = np.array([T_var(ti) for ti in t_grid]) - 273.15 # degC\n",
"\n",
"fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True, figsize=(7, 4.5))\n",
"ax1.plot(t_grid, T_grid, color='C3')\n",
"ax1.set_ylabel('T [degC]')\n",
"ax1.set_title('Exogenous temperature drives the decay rate')\n",
"ax2.plot(t_rec, S_S_rec, color='C0')\n",
"ax2.set_ylabel('S_S [mg/L]'); ax2.set_xlabel('time [d]')\n",
"fig.tight_layout()\n"
]
},
{
"cell_type": "markdown",
"id": "a89fa738",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"For convenience, `ExogenousDynamicVariable` also has a `classmethod` that enables batch creation of multiple variables at once. We just need to provide a file of the time-series data, including a column `t` for time points and additional columns of the variable values. See the [documentation](https://qsdsan.readthedocs.io/en/latest/api/utility_functions/dynamics.html#qsdsan.utils.ExogenousDynamicVariable.batch_init) of `ExogenousDynamicVariable.batch_init` for detailed usage."
]
},
{
"cell_type": "markdown",
"id": "d8205f55",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### 3.2. `DynamicInfluent`\n",
"[DynamicInfluent](https://qsdsan.readthedocs.io/en/latest/api/unit_operations/dynamic/DynamicInfluent.html) is a `SanUnit` subclass for generating dynamic influent streams from user-defined time-series data. The use of this class is, to some extent, similar to an `ExogenousDynamicVariable`."
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "7c2c9521",
"metadata": {
"execution": {
"iopub.execute_input": "2026-05-30T20:38:10.401324Z",
"iopub.status.busy": "2026-05-30T20:38:10.401324Z",
"iopub.status.idle": "2026-05-30T20:38:10.612920Z",
"shell.execute_reply": "2026-05-30T20:38:10.611914Z"
},
"scrolled": false,
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(