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CBE 30338 Data Analytics, Optimization, and Control
Course Information
Syllabus
Schedule
Assignments
Homework 1: Computing and Data Analysis Review
Lab 1: Step Test of a First-Order System
Lab 2: Relay (On-Off) Control
Lab 3: Model Identification
Lab 4: PI Control
Homework 2: Optimization
Lab 5: Open Loop Optimization
Lab 6: Model Predictive Control (MPC)
Topical Materials
1. What is Process Control?
1.1. What is Feedback?
1.2. Elements of Process Control
2. Process Modeling
2.1. One Compartment Pharmacokinetics
2.2. First-Order Linear Systems
2.3. First Order Model for a Single Heater
2.4. Fitting a Model to Experimental Data
2.5. Second Order Model
2.6. Fed-Batch Bioreactor
2.7. Characteristics of Second Order Systems
2.8. Exothermic Continuous Stirred Tank Reactor
2.9. Hare and Lynx Population Dynamics
2.10. Study Guide
3. Feedback Control
3.1. Case Study: Thermal Cycling for PCR
3.2. Setpoints
3.3. Case Study: PCR Thermal Cycler Protocols
3.4. Relay Control
3.5. Implementing Controllers in Python
3.6. Practical Proportional (P) and Proportional-Integral (PI) Control
3.7. Integral Windup and Bumpless Transfer
3.8. Analysis of Proportional Only Controller
3.9. Analysis of Proportional-Integral Controller
3.10. Controller Tuning
4. Process Analytics
4.1. Learning Goals
4.2. Data/Process/Operational Historian
4.3. State Estimation
4.4. Lab Assignment 5: State Estimation
4.5. Anomaly Detection
4.6. Lab Assignment 6: Anomaly Detection
4.7. Observer Synthesis using Linear Matrix Inequalities
4.8. Application of Luenberger Observers to Environmental Modeling of Rivers
4.9. Study Questions
5. Optimization
5.1. Linear Production Model
5.2. Linear Blending Problems
5.3. Homework 2: Optimization (Legacy)
5.4. Gasoline Blending
5.5. Linear Programming
5.6. Design of a Cold Weather Fuel
5.7. Pyomo Examples
5.8. Recharging Strategy for an Electric Vehicle
5.9. Pooling and Blending
6. Predictive Control
6.1. Simulation and Optimal Control in Pharmacokinetics
6.2. Static Operability
6.3. Open-Loop Optimal Control
6.4. Predictive Control
6.5. Implementing Predictive Control
6.6. Gompertz Model
6.7. Project: Gompertz Model for Tumor Growth
6.8. Time as a Decision Variable
6.9. TCLab: Open-Loop Optimization and Estimation using Pyomo
6.10. TCLab: Model Predictive Control (MPC)
7. Projects (Spring 2023)
7.1. Project Ideas
8. References
Control Laboratory
1. The Temperature Control Laboratory
1.1. Setting up TCLab
1.2. Testing and Troubleshooting TCLab
1.3. Python Coding for TCLab
1.4. Testing Your Software Environment and TCLab
2. Step Testing
2.1. Step Testing
2.2. Lab Assignment 1: Step Test of a First-Order System
3. Empirical Model Identification
3.1. Fitting Step Test Data to Empirical Models
4. Estimating Model Parameters
4.1. Two-Input, Two-Output Model
4.2. Two State Model for a Single Heater
4.3. Four State Model
4.4. Assignment: Model Identification
5. Feedback Control
5.1. Relay Control
5.2. Lab Assignment 4: Relay Control
5.3. Lab Assignment: PID Control
5.4. Lab Assignment 4: PI Control
6. State Estimation
6.2. Open and Closed Loop Estimation
7. Optimization
8. Predictive Control and Real Time Optimization
8.1. Simulation, Control, and Estimation using Pyomo
8.2. Simulation, Control, and Estimation using Pyomo
Python Tutorials
Python Tutorials
Tidy Data and Pandas
Coding Controllers with Python Generators
Modular Simulation using Python Generators
Animation in Jupyter Notebooks
Colab
Repository
Open issue
.ipynb
.pdf
Predictive Control and Real Time Optimization
8.
Predictive Control and Real Time Optimization
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