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001044516 005__ 20250725202235.0
001044516 037__ $$aFZJ-2025-03253
001044516 041__ $$aEnglish
001044516 1001_ $$0P:(DE-Juel1)198986$$aKuppa, Raman Ashoke$$b0$$eCorresponding author
001044516 1112_ $$a21st Symposium on Modeling and Experimental Validation$$cKarlsruhe$$d2025-03-11 - 2025-03-12$$gModVal 2025$$wGermany
001044516 245__ $$aData-driven approach for modeling and sensitivity analysis of a Proton-exchange membrane water electrolyzer
001044516 260__ $$c2025
001044516 3367_ $$033$$2EndNote$$aConference Paper
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001044516 3367_ $$2BibTeX$$aINPROCEEDINGS
001044516 3367_ $$2DRIVER$$aconferenceObject
001044516 3367_ $$2ORCID$$aLECTURE_SPEECH
001044516 3367_ $$0PUB:(DE-HGF)6$$2PUB:(DE-HGF)$$aConference Presentation$$bconf$$mconf$$s1753445109_29437$$xAfter Call
001044516 500__ $$aAcknowledgment: Financial support was provided by the German Federal Ministry of Education and Research (BMBF) within the H2Giga project DERIEL (grant number 03HY122C)
001044516 520__ $$aProton exchange membrane electrolytic cells (PEMEC) have emerged as a promising technology to produce environment friendly green hydrogen. These are complex systems which involve a highly non-linear interplay between mass transfer, fluid flow and electrochemical reactions. To improve the commercial viability of PEMECs, performance optimization of these systems using data-driven models has emerged as a promising approach [1]. In this work, we develop data-driven surrogate models using synthetic data obtained from a physics-based one-dimensional numerical model of PEMEC [2]. The predictive performance of three unique machine learning algorithms [3] (support vector regression (SVR), extreme gradient boosting (XGB) and artificial neural networks (ANN)) were evaluated and their ability to capture the inherent non-linearities of PEMEC were compared. Important transport properties of the system such as operating temperature, anode catalyst and porous transport layer thickness, electronic conductivity and membrane thickness were selected as the input features for developing the data-driven models. Both XGB and ANN models showed better performance in predicting the cell current density when compared to SVR model. The ANN model was deployed to conduct a parametric analysis to investigate the effect of operating conditions and transport properties. Furthermore, an explainable artificial intelligence technique known as SHAP (Shapely Additive Explanations) was employed to list out the important parameters influencing the cell current density. The SHAP analysis highlight that the influence of membrane thickness is higher than the electronic conductivity for supported catalyst layers, and conversely, for unsupported catalyst layers.
001044516 536__ $$0G:(DE-HGF)POF4-1231$$a1231 - Electrochemistry for Hydrogen (POF4-123)$$cPOF4-123$$fPOF IV$$x0
001044516 536__ $$0G:(DE-Juel1)HITEC-20170406$$aHITEC - Helmholtz Interdisciplinary Doctoral Training in Energy and Climate Research (HITEC) (HITEC-20170406)$$cHITEC-20170406$$x1
001044516 7001_ $$0P:(DE-Juel1)203319$$aHammacher, Linus$$b1$$ufzj
001044516 7001_ $$0P:(DE-Juel1)194150$$aKaryofylli, Violeta$$b2
001044516 7001_ $$0P:(DE-Juel1)161579$$aJodat, Eva$$b3
001044516 7001_ $$0P:(DE-Juel1)191359$$aKarl, André$$b4
001044516 7001_ $$0P:(DE-Juel1)156123$$aEichel, Rüdiger-A.$$b5$$ufzj
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001044516 9141_ $$y2025
001044516 920__ $$lyes
001044516 9201_ $$0I:(DE-Juel1)IET-1-20110218$$kIET-1$$lGrundlagen der Elektrochemie$$x0
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