Novel Framework for Simulated Moving Bed Reactor Optimization Based on Deep Neural Network Models and Metaheuristic Optimizers: An Approach with Optimality Guarantee

• Tipo: Artigo em Revista Científica Internacional
• Data: 2023

Autores

Santana, VV (Autor) Outra; Martins, MAF (Autor) Outra; Loureiro, JM (Autor) Outra; Ana M. Ribeiro (Autor) FEUP; Queiroz, LP (Autor) Outra; Rebello, CM (Autor) Outra; Rodrigues, AE (Autor) Outra; Nogueira, IBR (Autor) Outra

Revista

Título: ACS Omega

Vol. 8

Páginas: 6463-6475

Editora: American Chemical Society

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-00X-Z10

DOI: 10.1021/acsomega.2c06737

Abstract (EN): Model-based optimization of simulated moving bed reactors (SMBRs) requires efficient solvers and significant computational power. Over the past years, surrogate models have been considered for such computationally demanding optimization problems. In this sense, artificial neural networks-ANNs-have found applications for modeling the simulated moving bed (SMB) unit but not yet been reported for the reactive SMB (SMBR). Despite ANNs' high accuracy, it is essential to assess its capacity to represent the optimization landscape well. However, a consistent method for optimality assessment using surrogate models is still an open issue in the literature. As such, two main contributions can be highlighted: the SMBR optimization based on deep recurrent neural networks (DRNNs) and the characterization of the feasible operation region. This is done by recycling the data points from a metaheuristic technique-optimality assessment. The results demonstrate that the DRNN-based optimization can address such complex optimization while meeting optimality.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 13