A First Approach towards Adsorption-Oriented Physics-Informed Neural Networks: Monoclonal Antibody Adsorption Performance on an Ion-Exchange Column as a Case Study

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

Autores

Santana, VV (Autor) Outra; Gama, MS (Autor) Outra; Loureiro, JM (Autor) Outra; Rodrigues, AE (Autor) Outra; Ana M. Ribeiro (Autor) FEUP; Tavares, FW (Autor) Outra; Barreto, AG (Autor) Outra; Nogueira, IBR (Autor) Outra

Revista

A Revista está pendente de validação pelos Serviços Administrativos.

Título: CHEMENGINEERING

Vol. 116

Página Final: 21

ISSN: 2305-7084

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 1 Citação

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-JV1

DOI: 10.3390/chemengineering6020021

Abstract (EN): Adsorption systems are characterized by challenging behavior to simulate any numerical method. A novel field of study emerged within the numerical method in the last two years: the physics-informed neural network (PINNs), the application of artificial intelligence to solve partial differential equations. This is a complete new standpoint for solving engineering first-principle models, which up to that date was not explored in the field of adsorption systems. Therefore, this work proposed the evaluation of PINN to address the numerical solutions of a fixed-bed column where a monoclonal antibody is purified. The PINNs solution is compared with a traditional numerical method. The results show the accuracy of the proposed PINNs when compared with the numerical method. This points towards the potential of this technique to address complex numerical problems found in chemical engineering.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 9