Abstract

Multi-Parametric Optimization & Control

Model based multi-parametric optimization provides a complete map of solutions of an optimization problem as a function of, unknownbut bounded, parameters in the model, in a computationally efficient manner, without exhaustively enumerating the entire parameter space.

In a Model-based Predictive Control (MPC) framework, multi-parametric optimization can be used to obtain the governing control laws –the optimal control variables as an explicit function of the state variables. The main advantage of this approach is that it reduces repetitive on-line control and optimization to simple function evaluations, which can be implemented on simple computational hardware, such as a microchip, thereby opening avenues for many applications in chemical, energy, automotive, and biomedical equipment, devices and systems. In this presentation, we will first provide a historical progress report of the key developments in multi-parametric optimization and control. We will then describe PAROC, a systematic framework and prototype software system which allows for the representation, modelling and solution of integrated design, operation and advanced control problems. Its main features include: (i) a high-fidelity dynamic model representation, also involving global sensitivity analysis, parameter estimation and mixed integer dynamic optimization capabilities; (ii) a suite/toolbox of model approximation methods; (iii) a host of multi-parametric programming solvers (POP –parametric Optimization] for mixed continuous/integer problems; (iv) a state-space modelling representation capability for scheduling and control problems; and (v) an advanced control toolkit for multi-parametric/explicit MPC and moving horizon reactive scheduling problems. Algorithms that enable the integration capabilities of the systems for design, scheduling and control are presented along with applications in sustainable energy systems, process intensification, smart manufacturing and personalized healthcare engineering.

Biography

Professor Pistikopoulos is TEES Distinguished Research Professor in the Artie McFerrin Department of Chemical Engineering at Texas A&M University.

He was a Professor of Chemical Engineering at Imperial College London, UK (1991-2015) and the Director of its Centre for Process Systems Engineering (2002-2009). At Texas A&M, he is the Interim Co-Director and Deputy Director of the Texas A&M Energy Institute, the Course Director of the Master of Science in Energy, the Director of the Gulf Coast Regional Manufacturing Centre, and the Texas A&M Principal Investigator of the RAPID Manufacturing USA Institute on process intensification, co-leading the Modeling & Simulation Focus Area. He holds a Ph.D. degree from Carnegie Mellon University and he worked with Shell Chemicals in Amsterdam before joining Imperial. He has authored or co-authored over 400 major research publications in the areas of modelling, control and optimization of process, energy and systems engineering applications, 10 books and 2 patents. He is a co-founder of Process Systems Enterprise (PSE) Ltd, a Fellow of AIChE and IChemE and the current Editor-in-Chief of Computers & Chemical Engineering. He is the current Chair of the Computing and Systems Technology (CAST) Division of AIChE and he serves as a trustee of the Computer Aids for Chemical Engineering (CACHE) Organization. In 2007, Prof. Pistikopoulos was a co-recipient of the prestigious MacRobert Award from the Royal Academy of Engineering. In 2012, he was the recipient of the Computing in Chemical Engineering Award of CAST/AIChE. He received the title of Doctor Honoris Causa in 2014from the University Politehnica of Bucharest, and from the University of Pannonia in 2015. In 2013, he was elected Fellow of the Royal Academy of Engineering in the UK.