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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

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

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Author(s):
Chanona, E. A. del Rio [1] ; Petsagkourakis, P. [2] ; Bradford, E. [3] ; Graciano, J. E. Alves [4, 5] ; Chachuat, B. [1]
Total Authors: 5
Affiliation:
[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
Total Affiliations: 5
Document type: Journal article
Source: Computers & Chemical Engineering; v. 147, APR 2021.
Web of Science Citations: 0
Abstract

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)

FAPESP's process: 14/50279-4 - Brasil Research Centre for Gas Innovation
Grantee:Julio Romano Meneghini
Support Opportunities: Research Grants - Applied Research Centers Program