Intelligent optimization: Novel application of PCC, MCDM, and ANN + NSGA-III in integrated optimization of the flow field and porous layer structures for unitized regenerative fuel cell

Ke Chen, Wenshang Chen, Guofu Zou and Ben Chen

Applied Energy, 2024, vol. 374, issue C, No S0306261924013916

Abstract: Unitized Regenerative Fuel Cells (URFCs) are a promising technology that utilizes renewable energy sources to efficiently convert them into electricity while offering potential for renewable energy storage. These cells facilitate the conversion between electrical and chemical energy, enabling processes such as electrolysis and hydrogen gas synthesis from water. This study aims to propose a more efficient and stable mass transfer solution for URFCs through the integrated optimization of flow field structure and porous transport layer configuration. Leveraging Taguchi orthogonal design, Pearson correlation coefficient, contour analysis, multi-criteria decision making, and the integration of artificial neural networks with non-dominated sorting genetic algorithm-III, an optimized selection is performed. Results indicate that the optimized structure exhibits improved performance in both electrolytic cell (EC) and fuel cell (FC) modes. At a current density of 1.5 A/cm2, compared to traditional structures, the voltage decreases by 8.2 mV in the EC mode. In the FC mode at a current density of 1.0 A/cm2, performance improves by 5.748%, and URFC round-trip efficiency increases by 6.183%. The assessment of mass transfer capability reveals that the optimized structure promotes gas transfer processes in different modes, leading to significant overall performance enhancement of URFC. These findings provide valuable guidance for the enhanced performance of URFC.

Keywords: URFCs; Integrated optimization; Pearson correlation coefficient; Multi-criteria decision making; Artificial neural networks; Non-dominated sorting genetic algorithm-III (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261924013916
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:374:y:2024:i:c:s0306261924013916

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2024.124008

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().