TY  - CONF
AU  - Kuppa, Raman Ashoke
AU  - Hammacher, Linus
AU  - Karyofylli, Violeta
AU  - Jodat, Eva
AU  - Karl, André
AU  - Eichel, Rüdiger-A.
TI  - Data-driven approach for modeling and sensitivity analysis of a Proton-exchange membrane water electrolyzer
M1  - FZJ-2025-03253
PY  - 2025
N1  - Acknowledgment: Financial support was provided by the German Federal Ministry of Education and Research (BMBF) within the H2Giga project DERIEL (grant number 03HY122C)
AB  - Proton exchange membrane electrolytic cells (PEMEC) have emerged as a promising technology to produce environment friendly green hydrogen. These are complex systems which involve a highly non-linear interplay between mass transfer, fluid flow and electrochemical reactions. To improve the commercial viability of PEMECs, performance optimization of these systems using data-driven models has emerged as a promising approach [1]. In this work, we develop data-driven surrogate models using synthetic data obtained from a physics-based one-dimensional numerical model of PEMEC [2]. The predictive performance of three unique machine learning algorithms [3] (support vector regression (SVR), extreme gradient boosting (XGB) and artificial neural networks (ANN)) were evaluated and their ability to capture the inherent non-linearities of PEMEC were compared. Important transport properties of the system such as operating temperature, anode catalyst and porous transport layer thickness, electronic conductivity and membrane thickness were selected as the input features for developing the data-driven models. Both XGB and ANN models showed better performance in predicting the cell current density when compared to SVR model. The ANN model was deployed to conduct a parametric analysis to investigate the effect of operating conditions and transport properties. Furthermore, an explainable artificial intelligence technique known as SHAP (Shapely Additive Explanations) was employed to list out the important parameters influencing the cell current density. The SHAP analysis highlight that the influence of membrane thickness is higher than the electronic conductivity for supported catalyst layers, and conversely, for unsupported catalyst layers.
T2  - 21st Symposium on Modeling and Experimental Validation
CY  - 11 Mar 2025 - 12 Mar 2025, Karlsruhe (Germany)
Y2  - 11 Mar 2025 - 12 Mar 2025
M2  - Karlsruhe, Germany
LB  - PUB:(DE-HGF)6
UR  - https://juser.fz-juelich.de/record/1044516
ER  -