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EE6225 - Process Control

Learning Objective:

This course is intended to provide a review of modern process control engineering. The purpose of the course is to serve as an introduction to process dynamics, modeling and control. The objectives include: (a) equipping students with basic understanding of issues related to basic control algorithms, advanced control strategies, multivariable control, plant parameter estimation, and process modelling and simulation; (b) enhancing students’ skills and techniques for tackling practical process control system design problems through case studies.

Content:

Basic Control Algorithms. Model Predictive Control. Multivariable Control. Plant Parameter Estimation. Case Studies in Process Control.

Learning Outcome:

On completion of this course, students should be confident to handle tasks on modelling, analysis, design and implementation of control systems for the process industry.

Textbooks:

• J.M. Maciejowski, "Predictive Control with Constraints," Prentice Hall, 2001.
• D. E. Seborg, "Process Dynamics and Control," John Wiley & Sons, 2004.

References:

• Camacho and Bordons, "Model Predictive Control, 2nd Edition," Springer 2004.
• L. Wang, "Model Predictive Control System Design and Implementation Using MATLAB," Springer, 2009.
• Rossiter, "Model-Based Predictive Control: A Practical Approach," CRC Press, 2003.
• B. W. Bequette, "Process Control Modeling Design and Simulation," Prentice Hall, 2003.

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