| Texto completo | |
| Autor(es): |
Chanona, E. A. del Rio
[1]
;
Petsagkourakis, P.
[2]
;
Bradford, E.
[3]
;
Graciano, J. E. Alves
[4, 5]
;
Chachuat, B.
[1]
Número total de Autores: 5
|
| 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
Número total de Afiliações: 5
|
| 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 Aplicada |