Abstract
Roger W.H. Sargent (1926–2018) was an unprecedented pioneer who foresaw the role that mathematical and computational tools would have in chemical engineering. His visionary work created the multidisciplinary field of Process Systems Engineering (PSE), a field that acts as a central hub influencing all subfields of chemical engineering. His particular interest in optimal control applied to industrial processes led him to develop numerical techniques to solve large-scale optimal control problems. In this work, a brief overview of the theory of optimal control is offered, spanning from its roots in calculus of variations to Pontryagin's maximum principle and some of its extensions. Furthermore, important contributions made by Sargent and his students are presented. Selected applications currently found in literature are presented as well–ranging from classical chemical engineering systems to bioprocesses. Some future perspectives of the field are also presented in the concluding section.
Original language | English |
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Article number | 107528 |
Pages (from-to) | 107528 |
Journal | Computers and Chemical Engineering |
Volume | 155 |
DOIs | |
State | Published - Sep 2021 |
Bibliographical note
Funding Information:Author E. Nolasco gratefully acknowledges CONACYT-SENER for his PhD studentship. Author V. S. Vassiliadis wishes to dedicate this work to the memory of Professor Roger W.H. Sargent, who was his main Ph.D. supervisor at Imperial College between 1989 and 1993. Author W. K?hm would like to thank EPSRC and the Department of Chemical Engineering and Biotechnology, University of Cambridge, for his PhD studentship. Author S. D. Adloor would like to thank the Cambridge Trust (UK) and the Science and Engineering Research Board of India.
Publisher Copyright:
© 2021
Keywords
- Roger W.H. Sargent
- Optimal control
- Process systems engineering
- Calculus of variations
- Model predictive control
- Multistage optimal control problems
- Dynamic flux balance analysis
- Maintenance of decaying performance processes
- Global optimization