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© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

This paper reviews real-time optimization from a reinforcement learning point of view. The typical control and optimization system hierarchy depend on the layers of real-time optimization, supervisory control, and regulatory control. The literature about each mentioned layer is reviewed, supporting the proposal of a benchmark study of reinforcement learning using a one-layer approach. The multi-agent deep deterministic policy gradient algorithm was applied for economic optimization and control of the isothermal Van de Vusse reactor. The cooperative control agents allowed obtaining sufficiently robust control policies for the case study against the hybrid real-time optimization approach.

Details

Title
One-Layer Real-Time Optimization Using Reinforcement Learning: A Review with Guidelines
Author
Ruan de Rezende Faria 1   VIAFID ORCID Logo  ; Bruno Didier Olivier Capron 1   VIAFID ORCID Logo  ; Maurício B de Souza Jr 2   VIAFID ORCID Logo  ; Argimiro Resende Secchi 3   VIAFID ORCID Logo 

 Escola de Química, EPQB, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-909, Brazil 
 Escola de Química, EPQB, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-909, Brazil; Programa de Engenharia Química, PEQ/COPPE, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-972, Brazil 
 Programa de Engenharia Química, PEQ/COPPE, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-972, Brazil 
First page
123
Publication year
2023
Publication date
2023
Publisher
MDPI AG
e-ISSN
22279717
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2767265481
Copyright
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.