TY - JOUR
T1 - Two-phase analytical modeling and intelligence parameter estimation of proton exchange membrane electrolyzer for hydrogen production
AU - Wang, Bowen
AU - Ni, Meng
AU - Zhang, Shiye
AU - Liu, Zhi
AU - Jiang, Shangfeng
AU - Zhang, Longhai
AU - Zhou, Feikun
AU - Jiao, Kui
N1 - Funding Information:
This research is funded by Hong Kong Scholars Program (No. XJ2021033 ), and China Postdoctoral Science Foundation (No. 2021TQ0235 ).
Publisher Copyright:
© 2023 Elsevier Ltd
PY - 2023/7
Y1 - 2023/7
N2 - 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.
AB - 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.
KW - Anode catalyst layer
KW - Cathode high pressure
KW - Intelligence parameter estimation
KW - Liquid saturation jump
KW - Proton exchange membrane electrolyzer
KW - Two-phase characteristics
UR - http://www.scopus.com/inward/record.url?scp=85154040381&partnerID=8YFLogxK
U2 - 10.1016/j.renene.2023.04.090
DO - 10.1016/j.renene.2023.04.090
M3 - Journal article
AN - SCOPUS:85154040381
SN - 0960-1481
VL - 211
SP - 202
EP - 213
JO - Renewable Energy
JF - Renewable Energy
ER -