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| 001 | 1037215 | ||
| 005 | 20250203103207.0 | ||
| 024 | 7 | _ | |2 doi |a 10.2139/ssrn.4969962 |
| 037 | _ | _ | |a FZJ-2025-00548 |
| 082 | _ | _ | |a 330 |
| 100 | 1 | _ | |0 P:(DE-Juel1)198986 |a Kuppa, Raman Ashoke |b 0 |e Corresponding author |u fzj |
| 245 | _ | _ | |a Data-Driven Surrogate Modeling Framework for Performance Prediction and Sensitivity Analysis of a Proton Exchange Membrane Water Electrolyzer |
| 260 | _ | _ | |a [S.l.] |b Social Science Electronic Publ. |c 2024 |
| 336 | 7 | _ | |0 PUB:(DE-HGF)25 |2 PUB:(DE-HGF) |a Preprint |b preprint |m preprint |s 1736861465_15027 |
| 336 | 7 | _ | |2 ORCID |a WORKING_PAPER |
| 336 | 7 | _ | |0 28 |2 EndNote |a Electronic Article |
| 336 | 7 | _ | |2 DRIVER |a preprint |
| 336 | 7 | _ | |2 BibTeX |a ARTICLE |
| 336 | 7 | _ | |2 DataCite |a Output Types/Working Paper |
| 520 | _ | _ | |a Proton 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 |
| 536 | _ | _ | |0 G:(DE-HGF)POF4-1231 |a 1231 - Electrochemistry for Hydrogen (POF4-123) |c POF4-123 |f POF IV |x 0 |
| 536 | _ | _ | |0 G:(DE-Juel1)HITEC-20170406 |a HITEC - Helmholtz Interdisciplinary Doctoral Training in Energy and Climate Research (HITEC) (HITEC-20170406) |c HITEC-20170406 |x 1 |
| 700 | 1 | _ | |0 P:(DE-Juel1)203319 |a Hammacher, Linus |b 1 |u fzj |
| 700 | 1 | _ | |0 P:(DE-Juel1)157700 |a Kungl, Hans |b 2 |
| 700 | 1 | _ | |0 P:(DE-Juel1)191359 |a Karl, André |b 3 |
| 700 | 1 | _ | |0 P:(DE-Juel1)161579 |a Jodat, Eva |b 4 |
| 700 | 1 | _ | |0 P:(DE-Juel1)156123 |a Eichel, Rüdiger-A. |b 5 |u fzj |
| 700 | 1 | _ | |0 P:(DE-Juel1)194150 |a Karyofylli, Violeta |b 6 |
| 773 | _ | _ | |0 PERI:(DE-600)2234654-5 |a 10.2139/ssrn.4969962 |t SSRN eLibrary |x 1556-5068 |y 2024 |
| 856 | 4 | _ | |u https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4969962 |
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| 914 | 1 | _ | |y 2024 |
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| 920 | 1 | _ | |0 I:(DE-Juel1)IET-1-20110218 |k IET-1 |l Grundlagen der Elektrochemie |x 0 |
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| 980 | _ | _ | |a VDB |
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| 980 | _ | _ | |a UNRESTRICTED |
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