Ezra's Round Table/Systems Seminar: James Rawlings (Wisconsin) - Economic Model Predictive Control for Closed-Loop Chemical Reactor Scheduling

Location

Frank H. T. Rhodes Hall 253

Description

Traditionally, scheduling and control are viewed as two related but disparate engineering activities. For scheduling, the main decisions are typically discrete yes/no choices; the models capture only important discrete events and transitions but include many units; and, the objective is generally economic in some sense. For control, the decisions are almost always continuous in nature; the models describe detailed temporal dynamics of the system but are local in scope; and, the objective function is artificially designed to maintain the system at a predetermined setpoint. Despite these differences, both problems can be addressed by formulating a mathematical optimization and solving it repeatedly as new information is received. In this presentation, we advance the idea that certain classes of scheduling and control problems are indeed two variants of the same overall problem that differ only in their respective system dynamics and decision space. We discuss results showing that, with suitable assumptions, the presence of discrete-valued control inputs does not affect the stability properties of model predictive control (MPC). Combining these ideas, we show that economically optimizing scheduling and control problems can both be viewed as cases of dynamic real-time optimization or economic MPC, which has important ramifications for closed-loop implementation. Bio: 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 is currently the Steenbock Professor of Engineering and W. Harmon Ray Professor of Chemical and Biological 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: "Model Predictive Control: Theory Computation, and Design," 2nd ed. (2017), with David Mayne and Moritz Diehl, "Modeling and Analysis Principles for Chemical and Biological Engineers" (2013), with Mike Graham, and "Chemical Reactor Analysis and Design