CHEN4011 • Semester 2
Advanced Modeling and Control
Advanced control strategies for multivariable industrial processes, plus data-driven and machine learning modeling.
Fourth-year undergraduate unit at Curtin University in two parts: advanced control methods for dynamic, multivariable industrial processes using MATLAB Simulink, and advanced modeling focused on data analysis, empirical models, machine learning, and process optimization.
What you will learn
By the end of the unit, students are able to:
- Apply advanced control techniques to chemical and petrochemical processes.
- Explain multi-loop control design, including pairing and decoupling.
- Apply advanced modeling and solution techniques.
- Use MATLAB and Simulink for modeling and control design.
Part 1: Control
- Process control and modeling fundamentals
- Feedforward and ratio control
- Cascade control
- PID enhancements and advanced techniques
- MIMO systems and decentralised control
- Model predictive control and centralised multivariable control
- Introduction to digital control
Part 2: Modeling
- Time series modeling and analysis
- Principal component analysis
- Artificial neural networks
- Process optimization and data-driven approaches
Tools
MATLAB and Simulink are used throughout for controller design, simulation, and process modeling.
Before you start
Builds on introductory process control (CHEN3005 Process Instrumentation and Control), with a background in differential equations and material and energy balances.
Course site: amc.smilelab.dev