Two-phase analytical modeling and intelligence parameter estimation of proton exchange membrane electrolyzer for hydrogen production

Bowen Wang, Meng Ni, Shiye Zhang, Zhi Liu, Shangfeng Jiang, Longhai Zhang, Feikun Zhou and Kui Jiao

Renewable Energy, 2023, vol. 211, issue C, 202-213

Abstract: The proton exchange membrane electrolyzer (PEME) is a promising tool for hydrogen production, and internal two-phase transport significantly influences its performance. In this study, a two-phase analytical PEME model incorporating the liquid saturation jump effect was developed, and intelligent parameter estimation using a genetic algorithm was proposed to achieve high-efficiency model validation. In-house experiments and experimental results from numerous papers in the literature were employed to prove the effectiveness of the proposed intelligent parameter estimation. Moreover, the two-phase simulation results demonstrated that the PEME voltage increased significantly when the current density reached the limiting value, and the liquid saturation in the anode catalyst layer (ACL) dropped to nearly zero. Increasing ACL porosity, decreasing ACL permeability, and decreasing ACL thickness could increase the limiting current density within the investigated range. The simulated limiting current density could be > 5 A cm−2 through proper design of the ACL parameters. For high-pressure cathode operation, increasing the cathode pressure and membrane permeability generally benefits water management inside the PEME and therefore increases the limiting current density. This study provides critical support for the design of cells and operating conditions for future PEME studies.

Keywords: Proton exchange membrane electrolyzer; Intelligence parameter estimation; Two-phase characteristics; Liquid saturation jump; Anode catalyst layer; Cathode high pressure (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:211:y:2023:i:c:p:202-213

DOI: 10.1016/j.renene.2023.04.090

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