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| 001 | 1044516 | ||
| 005 | 20250725202235.0 | ||
| 037 | _ | _ | |a FZJ-2025-03253 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Kuppa, Raman Ashoke |0 P:(DE-Juel1)198986 |b 0 |e Corresponding author |
| 111 | 2 | _ | |a 21st Symposium on Modeling and Experimental Validation |g ModVal 2025 |c Karlsruhe |d 2025-03-11 - 2025-03-12 |w Germany |
| 245 | _ | _ | |a Data-driven approach for modeling and sensitivity analysis of a Proton-exchange membrane water electrolyzer |
| 260 | _ | _ | |c 2025 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a Other |2 DataCite |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 336 | 7 | _ | |a conferenceObject |2 DRIVER |
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| 336 | 7 | _ | |a Conference Presentation |b conf |m conf |0 PUB:(DE-HGF)6 |s 1753445109_29437 |2 PUB:(DE-HGF) |x After Call |
| 500 | _ | _ | |a Acknowledgment: Financial support was provided by the German Federal Ministry of Education and Research (BMBF) within the H2Giga project DERIEL (grant number 03HY122C) |
| 520 | _ | _ | |a 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. |
| 536 | _ | _ | |a 1231 - Electrochemistry for Hydrogen (POF4-123) |0 G:(DE-HGF)POF4-1231 |c POF4-123 |f POF IV |x 0 |
| 536 | _ | _ | |a HITEC - Helmholtz Interdisciplinary Doctoral Training in Energy and Climate Research (HITEC) (HITEC-20170406) |0 G:(DE-Juel1)HITEC-20170406 |c HITEC-20170406 |x 1 |
| 700 | 1 | _ | |a Hammacher, Linus |0 P:(DE-Juel1)203319 |b 1 |u fzj |
| 700 | 1 | _ | |a Karyofylli, Violeta |0 P:(DE-Juel1)194150 |b 2 |
| 700 | 1 | _ | |a Jodat, Eva |0 P:(DE-Juel1)161579 |b 3 |
| 700 | 1 | _ | |a Karl, André |0 P:(DE-Juel1)191359 |b 4 |
| 700 | 1 | _ | |a Eichel, Rüdiger-A. |0 P:(DE-Juel1)156123 |b 5 |u fzj |
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| 913 | 1 | _ | |a DE-HGF |b Forschungsbereich Energie |l Materialien und Technologien für die Energiewende (MTET) |1 G:(DE-HGF)POF4-120 |0 G:(DE-HGF)POF4-123 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-100 |4 G:(DE-HGF)POF |v Chemische Energieträger |9 G:(DE-HGF)POF4-1231 |x 0 |
| 914 | 1 | _ | |y 2025 |
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