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

• Type: Article in International Scientific Journal
• Date: 2022

Authors

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

Journal

The Journal is awaiting validation by the Administrative Services.

Title: CHEMENGINEERING

Vol. 116

Final page: 21

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 1 Citation

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 9