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

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

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plant-wide control of nonlinear systems; centralized and distributed economic model predictive control; operational safety and cybersecurity of closed-loop processes; reduced-order modeling in predictive control


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 and cybersecurity as central control system objectives, and have revealed the critical role of accounting for process equipment when seeking to improve 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 and techniques for their design using on-line operating data 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 an investigation of control system designs that explicitly respect safety objectives, and c) a nonlinear systems framework for analyzing cybersecurity for process control systems in the presence of falsified state measurements. Throughout the talk, we will present applications of our methods to chemical processes to demonstrate their applicability and performance in meeting next-generation manufacturing goals related to improving process economics, safety, and sustainability.




Helen Elaine Durand was born in Canoga Park, California.  She received her B.S. in Chemical Engineering from UCLA, and upon graduation 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, and her Ph.D. in Chemical Engineering from UCLA in 2017.  She is currently an Assistant Professor in the Department of Chemical Engineering and Materials Science at Wayne State University.  Her research interests are in the general area of process systems engineering with a focus on process control and process operational safety. 

Click here for more information about the Assistant Professor of Department of Chemical Engineering and Materials Science and Adjunct Assistant Professor of the Department of Electrical and Computer Engineering at Wayne State University.