Real-time optimization meets Bayesian optimization and derivative-free optimization: A tale of modifier adaptation

Autor(es)

Chanona, E. A. del Rio ^[1] ; Petsagkourakis, P. ^[2] ; Bradford, E. ^[3] ; Graciano, J. E. Alves ^{[4, 5]} ; Chachuat, B. ^[1]

Afiliação do(s) autor(es)

^[1] Imperial Coll London, Dept Chem Engn, Ctr Proc Syst Engn, London - England

^[2] UCL, Dept Chem Engn, Ctr Proc Syst Engn, London - England

^[3] Norwegian Univ Sci & Technol, Dept Engn Cybernet, Trondheim - Norway

^[4] Univ Sao Paulo, Dept Engn Quim, Escola Politecn, Sao Paulo - Brazil

^[5] Radix Engn & Software, Rio De Janeiro - Brazil

• Tipo de documento: Artigo Científico
• Fonte: Computers & Chemical Engineering; v. 147, APR 2021.
• Citações Web of Science: 0

Resumo

This paper investigates a new class of modifier-adaptation schemes to overcome plant-model mismatch in real-time optimization of uncertain processes. The main contribution lies in the integration of concepts from the fields of Bayesian optimization and derivative-free optimization. The proposed schemes embed a physical model and rely on trust-region ideas to minimize risk during the exploration, while employing Gaussian process regression to capture the plant-model mismatch in a non-parametric way and drive the exploration by means of acquisition functions. The benefits of using an acquisition function, knowing the process noise level, or specifying a nominal process model are analyzed on numerical case studies, including a semi-batch photobioreactor optimization problem with a dozen decision variables. (c) 2021 Elsevier Ltd. All rights reserved. (AU)

• Processo FAPESP: 14/50279-4 - Brasil Research Centre for Gas Innovation
• Beneficiário: Julio Romano Meneghini
• Modalidade de apoio: Auxílio à Pesquisa - Programa Centros de Pesquisa em Engenharia