Dr. Badgwell of PROFESSOR OF PRACTICE IN THE MCKETTA DEPARTMENT OF CHEMICAL ENGINEERING, THE UNIVERSITY OF TEXAS AT AUSTIN

In this presentation we highlight three emerging technologies that appear to be truly disruptive; that is, they are likely to have such a large impact that they will change the way theoreticians and practitioners think about and accomplish manufacturing and process control.  These technologies are Economic Model Predictive Control (EMPC), Deep Reinforcement Learning (DRL), and Open Process Automation (OPA).  Economic MPC (EMPC) is a relatively new technology that combines economic optimization with Model Predictive Control. This unification of closed-loop scheduling, economic optimization, and dynamic control provides a new platform for viewing and analyzing manufacturing problems. Reinforcement Learning (RL) is a Machine Learning (ML) technology in which a computer agent learns, through trial and error, the best way to accomplish a particular task. It is likely that DRL will take over some of the more mundane tasks involved in managing manufacturing systems, such as tuning of PID controllers, mitigating disturbances, and recovering from process upsets. Open Process Automation (OPA) will allow manufacturers, whose innovations have been constrained for decades by the limitations of closed, proprietary systems, to experience the benefits of open, interoperable, resilient, secure-by-design automation systems.  This will be made possible by the development of the consensus-based Open Process Automation Standard (O-PAS) by the Open Process Automation Forum (OPAF).

Biography:

Thomas A. (Tom) Badgwell, PhD, PE, is a Professor of Practice in the McKetta Department of Chemical Engineering at The University of Texas at Austin. He earned a BS degree from Rice University and MS and PhD degrees from the University of Texas at Austin, all in Chemical Engineering, and he is registered as a Professional Engineer in Texas. Tom’s career has focused on modeling, optimization, and control of chemical processes, with past positions at Setpoint, Fisher/Rosemount, Rice University, Aspen Technology, ExxonMobil, and Collaborative Systems Integration. He is a Fellow of the American Institute of Chemical Engineers (AIChE) and a past Director of the Computing and Systems Technology (CAST) Division, from which he received the Computing Practice Award in 2013. He is also a member of the IEEE Control System Society. Tom was inducted into the Control Global Process Automation Hall of Fame in 2022. He has served as an Associate Editor for the Journal of Process Control, as an Industrial Trustee of the Computer Aids in Chemical Engineering (CACHE) Corporation, as a Member of the IFAC Industry Committee, and is presently the Vice Chair, Industry on the IFAC Technical Committee (6.1) on Chemical Process Control. He has 5 patents, and his 25 refereed publications have received over 11,000 citations.

Email:

Address:Austin, United States