The Department of Chemical and Biological Engineering is proud to present our 20th annual Richard S.H. Mah Lecture with speaker Jim Rawlings from the University of California, Santa Barbara.
Process Engineering at the Dawn of (Generative) AI
Maintaining high standards of living while decreasing our impact on the planet requires both new manufacturing technologies as well as increasingly efficient operation of these technologies at large scale. Process systems engineering will play a critical role in addressing these challenges.
We start with a review of some of the advances that have taken place in the field of process systems engineering during the last 50 years. We focus specifically on process control because it is currently faced with several new challenges: increasingly stringent product quality requirements, tighter environmental regulations, and increasingly transient operating conditions, e.g., large fluctuations in electricity prices and supply chains. We present the central ideas of model predictive control, which has become during this period the leading advanced feedback control method, both in industrial practice as well as a topic of control theory research. We discuss the fundamental reasons for this success, which builds upon the foundations of optimal control and dynamic modeling, supplemented with measurement feedback to make the resulting system robust against model inaccuracies and disturbances.
At the dawn of generative AI, we anticipate using machine learning and deep neural networks trained on large datasets to provide improved modeling capability. But when we create models for process optimization or process control, the basic issue to be understood is how much information are we able to extract from available measurements, and how much domain-specific structural information must we provide, e.g., conservation laws and thermodynamic principles, before the optimization of such composite models is reliable and useful. We currently lack systematic guidelines for how best to combine these two sources of information.
Finally we discuss the education of chemical engineers to understand, operate and improve these new technologies. Adding to our educational challenge, universities are now seeing the first generation of engineering students who first ask a large language model (LLM) agent to solve their homework exercises or write their software code. Meanwhile most of these exercises were designed to be solved solely by the students, to start building up their engineering understanding and intuition through simple examples. This mismatch in current educational materials and widely-available LLM technology is a pressing challenge that educators must now address.
James B. Rawlings received the B.S. from the University of Texas and the Ph.D. from the University of Wisconsin, both in Chemical Engineering. He spent one year at the University of Stuttgart as a NATO postdoctoral fellow and then joined the faculty at the University of Texas. He moved to the University of Wisconsin in 1995, and then to the University of California, Santa Barbara in 2018, and is currently the Mellichamp Process Control Chair in the Department of Chemical Engineering, and the co-director of the Texas-Wisconsin-California Control Consortium (TWCCC).
Professor Rawlings's research interests are in the areas of chemical process modeling, monitoring and control, nonlinear model predictive control, moving horizon state estimation, and molecular-scale chemical reaction engineering. He has written numerous research articles and coauthored three textbooks: "Modeling and Analysis Principles for Chemical and Biological Engineers," 2nd ed. (2022), with Mike Graham, "Model Predictive Control: Theory Computation, and Design," 2nd ed. (2020), with David Mayne and Moritz Diehl, and "Chemical Reactor Analysis and Design Fundamentals," 2nd ed. (2020), with John Ekerdt.
In recognition of his research and teaching, Professor Rawlings has
received several awards including:
- Election to the National Academy of Engineering;
- John M. Prausnitz Institute Lecturer, AIChE
- Richard E. Bellman Control Heritage Award, American Automatic Control Council
- William H. Walker Award for Excellence in Contributions to Chemical
- Engineering Literature from the AIChE;
- Warren K. Lewis Award for Chemical Engineering Education from the AIChE;
- Inaugural High Impact Paper Award from the International Federation of Automatic Control (IFAC);
- Ragazzini Education Award, American Automatic Control Council;
He is a fellow of IFAC, IEEE, and AIChE.
Dr. Rawlings' Mah Lecture will be hosted by department chair Danielle Tullman-Ercek.
Cost: Free
Olivia Wise
Email