Using scientific machine learning to develop universal differential equation for multicomponent adsorption separation systems

• Tipo: Artigo em Revista Científica Internacional
• Data: 2022

Autores

Nogueira, IBR (Autor) Outra; Santana, VV (Autor) Outra; Ana M. Ribeiro (Autor) FEUP; Rodrigues, AE (Autor) Outra

Revista

Título: Canadian Journal of Chemical Engineering

Vol. 100

Páginas: 2279-2290

ISSN: 0008-4034

Editora: Wiley-Blackwell

Indexação

ISI Web of Knowledge - 0 Citações

Scopus

Projetos Associados

LA LSRE-LCM - Financiamento Plurianual 2020-2023 para a Unidade LA LSRE-LCM Laboratório de Processos de Separação e Reacção - Laboratório de Catálise e Materiais

Outras Informações

ID Authenticus: P-00W-XA3

DOI: 10.1002/cjce.24495

Abstract (EN): Universal differential equations are a concept in scientific machine learning that leverages the potential of the universal approximator theorem and the physical knowledge of a given system. Creating this level of hybridization within a stiff partial differential equation system is a challenge. On the other hand, adsorption phenomenological models have sink/source terms that describe the adsorption equilibrium through a well-known simplified model (e.g., Langmuir; Sips; and Brunauer, Emmet, Teller [BET]). These suitable mechanistic assumptions are identified through experiments, providing the parameters of the sink/source model. However, these mechanistic assumptions are a simplification of the system phenomenology. Therefore, the resulting model is limited by its premises. In this scenario, the universal ordinary differential equations (UODE) is presented as an approach that conciliates the potential of artificial neural networks to learn given phenomena without conceptual simplifications. On the other hand, keeping into consideration the system physics. This work proposes a UODE system to solve the multicomponent separation by adsorption in a fixed bed column. Experimental data is used to identify the hybrid model. The required amount of data used in the model identification demonstrates that hybrid models can use a few data points to precisely describe the system. Furthermore, the obtained model can describe competitive adsorption with higher precision than the Langmuir model.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 12