Model Predictive Control (MPC) is the most commonly advanced control technique applied in the chemical process industries. In this Webinar, basic feedback control principles are reviewed using a simple surge tank example. Important challenges in petroleum refining and other large-scale chemical processes, which largely led to the development of MPC algorithms, are discussed. Next, the fundamental framework for MPC, based on optimization concepts, is developed, and low-order, constrained examples are used to illustrate the main points. Finally, selected MPC applications, including the development of a closed-loop artificial pancreas, are presented.
Dr. Bequette worked for three years as a process engineer for American Petrofina between his undergraduate and graduate studies. From 1986 to 1987 he was a postdoctoral research associate at the University of Texas and from 1987 to 1988 he was a Visiting Lecturer at the University of California at Davis. He joined the Rensselaer faculty in 1988 and was promoted to Associate Professor in 1994 and Professor in 2000. Dr. Bequette was on sabbatical leave during the 1995-6 academic year, spending the fall semester at Merck Research Laboratories and the spring at Northwestern University.