Multi-parametric Optimization and Control. Efstratios N. Pistikopoulos

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       Library of Congress Cataloging‐in‐Publication Data

      Names: Pistikopoulos, Efstratios N., author.

      Title: Multi‐parametric optimization and control / Efstratios N.

      Pistikopoulos, Nikolaos A. Diangelakis, Richard Oberdieck.

      Description: First edition. | Hoboken, NJ : Wiley, 2021. | Series: Wiley

      series in operations research and management science | Includes

      bibliographical references and index.

      Identifiers: LCCN 2020024011 (print) | LCCN 2020024012 (ebook) | ISBN

      9781119265184 (hardback) | ISBN 9781119265153 (adobe pdf) | ISBN

      9781119265191 (epub)

      Subjects: LCSH: Mathematical optimization–Computer programs.

      Classification: LCC QA402.5 .P558 2021 (print) | LCC QA402.5 (ebook) |

      DDC 519.7–dc23

      LC record available at https://lccn.loc.gov/2020024011

      LC ebook record available at https://lccn.loc.gov/2020024012

      Cover Design: Wiley

      Cover Image: Courtesy of Professor Pistikopoulos'research group

       To the Memory and Legacy of Professor Christodoulos A. Floudas

Short Bios of the Authors

      Efstratios N. Pistikopoulos

Professor Pistikopoulos is the Director of the Texas A&M Energy Institute and a TEES Eminent 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). He holds a PhD degree from Carnegie Mellon University and he worked with Shell Chemicals in Amsterdam before joining Imperial. He has authored or co‐authored over 500 major research publications in the areas of modeling, control and optimization of process, and energy and systems engineering applications, 12 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. In 2007, he 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 from the University Politehnica of Bucharest in 2014, and from the University of Pannonia in 2015. In 2013, he was elected Fellow of the Royal Academy of Engineering in the United Kingdom.

Nikolaos A. Diangelakis

Dr. Diangelakis is an Optimization Specialist at Octeract Ltd. in London, UK, a massively parallel global optimization software firm. He was a postdoctoral research associate at Texas A&M University and Texas A&M Energy Institute. He holds a PhD and M.Sc. on Advanced Chemical Engineering from Imperial College London and has been a member of the “Multi‐parametric Optimization and Control Group” since late 2011. He earned his bachelor degree in 2011 from the National Technical University of Athens (NTUA). His main research interests are on the area of optimal receding horizon strategies for chemical and energy processes while simultaneously optimizing their design. For that purpose, he is investigating novel solution methods for classes of non‐linear, robust, and multi‐parametric optimization programming problems. He is the main developer of the PARametric Optimization and Control (PAROC) platform and co‐developer of the Parametric OPtimization (POP) toolbox. In 2016 he was chosen as one of five participants in the “Distinguished Junior Researcher Seminars” in Northwestern University, organized by Prof. Fengqi You. In 2017 he received the third place in EFCE's “Excellence Award in Recognition of Outstanding PhD Thesis on CAPE.” He is the coauthor of 16 peer reviewed articles, 11 conference papers and 3 book chapters.

Richard Oberdieck

      Richard Oberdieck is a Technical Account Manager at Gurobi Optimization, LLC, one of the leading mathematical optimization software companies. He obtained a bachelor and MSc degrees from ETH Zurich in Switzerland (2009–1013), before pursuing a PhD in Chemical Engineering at Imperial College London, UK, which he completed in 2017. During is PhD, he discovered fundamental properties of multi‐parametric programming problems and implemented them in the Parametric Optimization (POP) toolbox, of which he was the main developer. After using his knowledge in mathematical modeling and optimization in the space of renewable energies at the world leader in offshore wind energy, Ørsted A/S, he is now helping companies around the world unlock business value through mathematical optimization as a Technical Account Manager for Gurobi Optimization, LLC. He has published 21 papers and 2 book chapters, has an h‐index of 11 and was awarded the FICO Decisions Award 2019 in Optimization, Machine Learning and AI.

Preface

      Many optimization problems involve parameters that are unknown, either because they cannot be measured, or because they represent information about the future (e.g. future state of a system, future disturbance, future demand). Multi‐parametric programming is a technique for the solution of such class of uncertain optimization problems. Through multi‐parametric programming, one can obtain the optimization variables of the problem as a function of the bounded uncertain parameters, and the regions (in the space of the parameters) where these functions are valid.

      Theoretic and algorithmic developments on multi‐parametric programming, along with applications in the area of process systems engineering, have been constantly emerging during the last 30 years.

      A variety of algorithms for the solution of a range of classes of multi‐parametric programming problems have been developed, with our group publishing over 80 manuscripts, 21 books and book chapters, and 2 patents on the subject. We have further developed a MATLAB^© based toolbox, POP^©, for the solution of various classes of multi‐parametric programming and a framework, PAROC^©, for the development of explicit model predictive controllers.

This book aims to enable fundamental understanding in the areas of multi‐parametric optimization and control. We hope that by the end of the book, the reader will be able to not only understand almost all aspects of multi‐parametric programming, but also judge the key characteristics and particulars of the various techniques developed for different mathematical programming problems, use the tools to solve parametric problems, and finally, develop explicit model predictive controllers.

      The book begins with an introduction to the fundamentals of optimization and the basic theories and definitions used in multi‐parametric optimization. Then, two main parts follow, providing a clear distinction between algorithmic developments and