Optimization-Based Control of Nonlinear Processes: Fundamental Advances Enabling Economically-Optimal and Safe Process Operation

Speaker: Helen Durand
Affiliation: Ph.D. Candidate - UCLA

Abstract:  While most undergraduate process control courses focus on the dynamics and control of chemical processes that can be described by linear transfer function models, advances in computing over the last several decades have enabled the more complex nature (i.e., nonlinearities, interactions between variables, constraints) of the underlying process physico-chemical phenomena to be taken into account in the models used for controller design. A major trend in industry over the last 40 years has been employing constrained mathematical optimization techniques to compute control actions that optimize a quadratic objective function with its minimum at a process steady-state, subject to linear or nonlinear process models and practical constraints such as bounds on flow rates due to valve limitations. These optimization-based control designs (referred to as model predictive control or MPC) are typically implemented within a two-layer architecture. The upper layer (referred to as real-time optimization or RTO) solves an optimization problem that determines the economically-optimal operating steady-state for the process given recent process data, and then communicates its solution to an MPC system that computes values of the control actions that drive the process state to the economically-optimal steady-state. Despite the benefits of the current control architecture, recent trends in the process industries have motivated tighter integration of the economic optimization and feedback control layers to improve process economics with off steady-state operation, have established process safety as a central control system objective, and have revealed the critical role of control valve function in achieving the theoretically-predicted closed-loop performance.

These trends have motivated our research towards the development of rigorous, yet broadly applicable, optimization-based control methods for nonlinear processes. Specifically, we will present: a) feedback control designs that directly optimize non-quadratic economics-based cost functions while accounting for a broad set of actuator constraints; b) a control-theoretic approach to process operational safety which leads to control system designs that explicitly respect safety objectives; and c) an elucidation of the negative impact of sticky control valves on closed-loop performance and controller re-design strategies that eliminate these impacts. Throughout the talk, we will present applications of our methods to chemical processes of industrial interest to demonstrate their applicability and performance in meeting next-generation manufacturing goals related to improving process economics, safety, and sustainability.

Biography:  Helen Durand was born in Canoga Park, California. She received her B.S. in Chemical Engineering from UCLA and subsequently joined the Materials & Processes Engineering Department as an engineer at Aerojet Rocketdyne for two and a half years. She earned her M.S. in Chemical Engineering from UCLA in 2014.  Her research interests are in the general area of process systems engineering with a focus on process control. Upon graduating from UCLA with her Ph.D. in Chemical Engineering, she will join Wayne State University as an Assistant Professor in the Department of Chemical Engineering and Materials Science.

For more information, contact Prof. Panagiotis D. Christofides ()

Date(s) - May 18, 2017
11:00 am - 1:00 pm

E-IV Maxwell Room #57-124
420 Westwood Plaza - 5th Flr. , Los Angeles CA 90095