{ "cells": [ { "cell_type": "markdown", "metadata": { "nbpages": { "level": 0, "link": "[](https://jckantor.github.io/CBE32338/05.02-Optimization-Control-and-Estimation-using-Pyomo-With-Windows-ipopt.html)", "section": "" } }, "source": [ "\n", "*This notebook contains material from [CBE32338](https://jckantor.github.io/CBE32338);\n", "content is available [on Github](https://github.com/jckantor/CBE32338.git).*\n" ] }, { "cell_type": "markdown", "metadata": { "nbpages": { "level": 0, "link": "[](https://jckantor.github.io/CBE32338/05.02-Optimization-Control-and-Estimation-using-Pyomo-With-Windows-ipopt.html)", "section": "" } }, "source": [ "\n", "< [5.1 Simulation, Control, and Estimation using Pyomo](https://jckantor.github.io/CBE32338/05.01-Optimization-Control-and-Estimation-using-Pyomo.html) | [Contents](toc.html) | [A.0 Additional Python](https://jckantor.github.io/CBE32338/A.00-Additional-Python.html) >
"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpages": {
"level": 1,
"link": "[5.2 Simulation, Control, and Estimation using Pyomo](https://jckantor.github.io/CBE32338/05.02-Optimization-Control-and-Estimation-using-Pyomo-With-Windows-ipopt.html#5.2-Simulation,-Control,-and-Estimation-using-Pyomo)",
"section": "5.2 Simulation, Control, and Estimation using Pyomo"
}
},
"source": [
"# 5.2 Simulation, Control, and Estimation using Pyomo"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpages": {
"level": 2,
"link": "[5.2.1 Installations](https://jckantor.github.io/CBE32338/05.02-Optimization-Control-and-Estimation-using-Pyomo-With-Windows-ipopt.html#5.2.1-Installations)",
"section": "5.2.1 Installations"
}
},
"source": [
"## 5.2.1 Installations\n",
"\n",
"The following instructions show how to download and install pyomo and the ipopt solver. Execute the appropriate cell for your platform (if needed)."
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpages": {
"level": 3,
"link": "[5.2.1.1 Google Colab](https://jckantor.github.io/CBE32338/05.02-Optimization-Control-and-Estimation-using-Pyomo-With-Windows-ipopt.html#5.2.1.1-Google-Colab)",
"section": "5.2.1.1 Google Colab"
}
},
"source": [
"### 5.2.1.1 Google Colab"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"nbpages": {
"level": 3,
"link": "[5.2.1.1 Google Colab](https://jckantor.github.io/CBE32338/05.02-Optimization-Control-and-Estimation-using-Pyomo-With-Windows-ipopt.html#5.2.1.1-Google-Colab)",
"section": "5.2.1.1 Google Colab"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/bin/sh: wget: command not found\n",
"unzip: cannot find or open ipopt-linux64, ipopt-linux64.zip or ipopt-linux64.ZIP.\n"
]
}
],
"source": [
"!pip install -q pyomo\n",
"!wget -N -q \"https://ampl.com/dl/open/ipopt/ipopt-linux64.zip\"\n",
"!unzip -o -q ipopt-linux64\n",
"ipopt_executable = '/content/ipopt'"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpages": {
"level": 3,
"link": "[5.2.1.2 MacOS](https://jckantor.github.io/CBE32338/05.02-Optimization-Control-and-Estimation-using-Pyomo-With-Windows-ipopt.html#5.2.1.2-MacOS)",
"section": "5.2.1.2 MacOS"
}
},
"source": [
"### 5.2.1.2 MacOS"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"nbpages": {
"level": 3,
"link": "[5.2.1.2 MacOS](https://jckantor.github.io/CBE32338/05.02-Optimization-Control-and-Estimation-using-Pyomo-With-Windows-ipopt.html#5.2.1.2-MacOS)",
"section": "5.2.1.2 MacOS"
}
},
"outputs": [],
"source": [
"!pip install -q pyomo\n",
"!curl -s https://ampl.com/dl/open/ipopt/ipopt-osx.zip --output ipopt-osx.zip\n",
"!tar xf ipopt-osx.zip ipopt\n",
"ipopt_executable = \"./ipopt\"\n",
"!rm ipopt-osx.zip"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpages": {
"level": 3,
"link": "[5.2.1.3 Windows PC](https://jckantor.github.io/CBE32338/05.02-Optimization-Control-and-Estimation-using-Pyomo-With-Windows-ipopt.html#5.2.1.3-Windows-PC)",
"section": "5.2.1.3 Windows PC"
}
},
"source": [
"### 5.2.1.3 Windows PC"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"nbpages": {
"level": 3,
"link": "[5.2.1.3 Windows PC](https://jckantor.github.io/CBE32338/05.02-Optimization-Control-and-Estimation-using-Pyomo-With-Windows-ipopt.html#5.2.1.3-Windows-PC)",
"section": "5.2.1.3 Windows PC"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting package metadata: ...working... done\n",
"Solving environment: ...working... done\n",
"\n",
"# All requested packages already installed.\n",
"\n",
"Collecting package metadata: ...working... done\n",
"Solving environment: ...working... done\n",
"\n",
"# All requested packages already installed.\n",
"\n"
]
}
],
"source": [
"!conda install -c conda-forge pyomo pyomo.extras\n",
"!conda install -c conda-forge/label/cf201901 ipopt "
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpages": {
"level": 2,
"link": "[5.2.2 Process Information](https://jckantor.github.io/CBE32338/05.02-Optimization-Control-and-Estimation-using-Pyomo-With-Windows-ipopt.html#5.2.2-Process-Information)",
"section": "5.2.2 Process Information"
}
},
"source": [
"## 5.2.2 Process Information\n",
"\n",
"\\begin{align*}\n",
"C_p^H \\frac{dT_H}{dt} & = U_a (T_{amb} - T_H) + U_c (T_S - T_H) + P u(t) + d(t)\\\\\n",
"C_p^S \\frac{dT_S}{dt} & = - U_c (T_S - T_H) \n",
"\\end{align*}\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpages": {
"level": 3,
"link": "[5.2.2.1 Process Parameter Values](https://jckantor.github.io/CBE32338/05.02-Optimization-Control-and-Estimation-using-Pyomo-With-Windows-ipopt.html#5.2.2.1-Process-Parameter-Values)",
"section": "5.2.2.1 Process Parameter Values"
}
},
"source": [
"### 5.2.2.1 Process Parameter Values"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"nbpages": {
"level": 3,
"link": "[5.2.2.1 Process Parameter Values](https://jckantor.github.io/CBE32338/05.02-Optimization-Control-and-Estimation-using-Pyomo-With-Windows-ipopt.html#5.2.2.1-Process-Parameter-Values)",
"section": "5.2.2.1 Process Parameter Values"
}
},
"outputs": [],
"source": [
"P = 0.04 # power input when the system is turned\n",
"Ua = 0.068 # heat transfer coefficient from heater to environment\n",
"CpH = 6.50 # heat capacity of the heater (J/deg C)\n",
"CpS = 1.25 # heat capacity of the sensor (J/deg C)\n",
"Uc = 0.036 # heat transfer coefficient from heater to sensor\n",
"Tamb = 21.0 # ambient room temperature"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpages": {
"level": 3,
"link": "[5.2.2.2 Process Inputs](https://jckantor.github.io/CBE32338/05.02-Optimization-Control-and-Estimation-using-Pyomo-With-Windows-ipopt.html#5.2.2.2-Process-Inputs)",
"section": "5.2.2.2 Process Inputs"
}
},
"source": [
"### 5.2.2.2 Process Inputs\n",
"\n",
"The next cell defines some process inputs that will be used throughout the notebook to demonstrate aspects of process simulation, control, and estimation. These are gathered in one place to make it easier to modify the notebook to test the response under different conditions. These functions are implemented using the `interp1d` from the `scipy` library."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"nbpages": {
"level": 3,
"link": "[5.2.2.2 Process Inputs](https://jckantor.github.io/CBE32338/05.02-Optimization-Control-and-Estimation-using-Pyomo-With-Windows-ipopt.html#5.2.2.2-Process-Inputs)",
"section": "5.2.2.2 Process Inputs"
}
},
"outputs": [
{
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\n",
"text/plain": [
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}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.6.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
}