Adaptive digital twin for pressure swing adsorption systems: Integrating a novel feedback tracking system, online learning and uncertainty assessment for enhanced performance

• Type: Article in International Scientific Journal
• Date: 2024

Authors

Costa, EA (Author) Other; Rebello, CM (Author) Other; Schnitman, L (Author) Other; Loureiro, JM (Author) Other; Ana M. Ribeiro (Author) FEUP; Nogueira, IBR (Author) Other

Journal

Title: Engineering Applications of Artificial Intelligence

Vol. 127

ISSN: 0952-1976

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 0 Citations

Associated Projects

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

Other information

Authenticus ID: P-00Z-AAK

DOI: 10.1016/j.engappai.2023.107364

Abstract (EN): This paper presents a novel approach to digitizing and modeling pressure swing adsorption (PSA) processes using an uncertainty-aware digital twin. PSA modeling presents unique challenges due to its complex and cyclic behavior, which lacks a steady state. By contributing to the literature on periodic systems, we provide valuable insights into the potential applications of artificial intelligence and digital twins beyond the field of cyclic processes. Our proposed methodology can enhance the understanding and optimization of complex systems across various industries and applications. The proposed digital twin is uncertainty-aware and reliable, continuously updating itself through online learning and utilizing a novel feedback tracker to accurately represent the PSA system. This robust and adaptable methodology supports optimal PSA system operation and facilitates informed decision-making for enhanced process operation. The results demonstrate that the proposed approach yields a reliable digital twin for the PSA unit, capable of tracking the process's complex dynamics and adapting to changes, including adsorbent degradation, which is a significant challenge in PSA operations. Overall, this work highlights the potential of advanced technologies, such as digital twins and artificial intelligence, to improve performance and efficiency in the field of process engineering. This work contributes to the ongoing efforts to optimize industrial processes and support sustainable development by providing a reliable and adaptable methodology for digitizing PSA processes.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 15