001037215 001__ 1037215
001037215 005__ 20250203103207.0
001037215 0247_ $$2doi$$a10.2139/ssrn.4969962
001037215 037__ $$aFZJ-2025-00548
001037215 082__ $$a330
001037215 1001_ $$0P:(DE-Juel1)198986$$aKuppa, Raman Ashoke$$b0$$eCorresponding author$$ufzj
001037215 245__ $$aData-Driven Surrogate Modeling Framework for Performance Prediction and Sensitivity Analysis of a Proton Exchange Membrane Water Electrolyzer
001037215 260__ $$a[S.l.]$$bSocial Science Electronic Publ.$$c2024
001037215 3367_ $$0PUB:(DE-HGF)25$$2PUB:(DE-HGF)$$aPreprint$$bpreprint$$mpreprint$$s1736861465_15027
001037215 3367_ $$2ORCID$$aWORKING_PAPER
001037215 3367_ $$028$$2EndNote$$aElectronic Article
001037215 3367_ $$2DRIVER$$apreprint
001037215 3367_ $$2BibTeX$$aARTICLE
001037215 3367_ $$2DataCite$$aOutput Types/Working Paper
001037215 520__ $$aProton exchange membrane electrolytic cell (PEMEC) are complex multivariate electrochemical systems that have emerged as a prominent technology for generating green hydrogen. To reduce costs and accelerate the commercial deployment of PEMECs, it is crucial to develop accurate predictive models that enable to capture the inherent nonlinearities of PEM electrolyzers efficiently. Therefore, in this study, we develop data-based surrogate models for PEMEC with supported and unsupported catalyst layers using support vector regression (SVR), extreme gradient boosting (XGB), and artificial neural networks (ANN) machine learning techniques. These models are developed by using the datasets obtained from an analytical model and a physics-based one-dimensional numerical model of PEMEC. The dataset obtained from the one-dimensional model was split into datasets for supported and unsupported catalyst layers, based on the electronic conductivity of the anodecatalyst. The performance prediction of these three models is evaluated and compared with physics-based modeling results. We find that both ANN and XGB models performwell in predicting the cell current density. Therefore, the ANN model is selected to perform parametric analysis to investigate the effect of operating conditions and transport properties of the anode side. Both SHAP and sensitivity analysis reveal that the operating temperature is the most important parameter affecting the performance of the PEMEC. For supported catalyst layers, the influence of membrane thickness is greater than the catalyst’s electronic conductivity. However, in the case of unsupported catalysts layers, the SHAP values for electronic conductivity are found to be larger than membrane thickness
001037215 536__ $$0G:(DE-HGF)POF4-1231$$a1231 - Electrochemistry for Hydrogen (POF4-123)$$cPOF4-123$$fPOF IV$$x0
001037215 536__ $$0G:(DE-Juel1)HITEC-20170406$$aHITEC - Helmholtz Interdisciplinary Doctoral Training in Energy and Climate Research (HITEC) (HITEC-20170406)$$cHITEC-20170406$$x1
001037215 7001_ $$0P:(DE-Juel1)203319$$aHammacher, Linus$$b1$$ufzj
001037215 7001_ $$0P:(DE-Juel1)157700$$aKungl, Hans$$b2
001037215 7001_ $$0P:(DE-Juel1)191359$$aKarl, André$$b3
001037215 7001_ $$0P:(DE-Juel1)161579$$aJodat, Eva$$b4
001037215 7001_ $$0P:(DE-Juel1)156123$$aEichel, Rüdiger-A.$$b5$$ufzj
001037215 7001_ $$0P:(DE-Juel1)194150$$aKaryofylli, Violeta$$b6
001037215 773__ $$0PERI:(DE-600)2234654-5$$a10.2139/ssrn.4969962$$tSSRN eLibrary$$x1556-5068$$y2024
001037215 8564_ $$uhttps://papers.ssrn.com/sol3/papers.cfm?abstract_id=4969962
001037215 909CO $$ooai:juser.fz-juelich.de:1037215$$pVDB
001037215 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)198986$$aForschungszentrum Jülich$$b0$$kFZJ
001037215 9101_ $$0I:(DE-588b)36225-6$$6P:(DE-Juel1)198986$$aRWTH Aachen$$b0$$kRWTH
001037215 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)203319$$aForschungszentrum Jülich$$b1$$kFZJ
001037215 9101_ $$0I:(DE-588b)36225-6$$6P:(DE-Juel1)203319$$aRWTH Aachen$$b1$$kRWTH
001037215 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)157700$$aForschungszentrum Jülich$$b2$$kFZJ
001037215 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)191359$$aForschungszentrum Jülich$$b3$$kFZJ
001037215 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)161579$$aForschungszentrum Jülich$$b4$$kFZJ
001037215 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)156123$$aForschungszentrum Jülich$$b5$$kFZJ
001037215 9101_ $$0I:(DE-588b)36225-6$$6P:(DE-Juel1)156123$$aRWTH Aachen$$b5$$kRWTH
001037215 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)194150$$aForschungszentrum Jülich$$b6$$kFZJ
001037215 9131_ $$0G:(DE-HGF)POF4-123$$1G:(DE-HGF)POF4-120$$2G:(DE-HGF)POF4-100$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-1231$$aDE-HGF$$bForschungsbereich Energie$$lMaterialien und Technologien für die Energiewende (MTET)$$vChemische Energieträger$$x0
001037215 9141_ $$y2024
001037215 920__ $$lyes
001037215 9201_ $$0I:(DE-Juel1)IET-1-20110218$$kIET-1$$lGrundlagen der Elektrochemie$$x0
001037215 980__ $$apreprint
001037215 980__ $$aVDB
001037215 980__ $$aI:(DE-Juel1)IET-1-20110218
001037215 980__ $$aUNRESTRICTED