Machine learning in PEM water electrolysis: A study of hydrogen production and operating parameters

•Advanced machine learning models, such as RF, SVM, and XGBoost, demonstrate high accuracy in predicting hydrogen production rates in PEM electrolyzers.•RF models consistently outperformed the other models, showing exceptional predictive accuracy evidenced from the R² score of 0.9898, RMSE of 19.99...

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Veröffentlicht in:	Computers & chemical engineering Jg. 194; S. 108954
Hauptverfasser:	Shomope, Ibrahim, Al-Othman, Amani, Tawalbeh, Muhammad, Alshraideh, Hussam, Almomani, Fares
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.03.2025
Schlagworte:	eXtreme gradient boosting algorithm Hydrogen production Machine learning Random forest Support vector machine Water electrolysis Water electrolysis Random forest Support vector machine eXtreme gradient boosting algorithm Hydrogen production Machine learning
ISSN:	0098-1354
Online-Zugang:	Volltext
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