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

Abstract

Proton exchange membrane water electrolysis (PEMWE) powered by renewable energy stands out as a promising technology for the sustainable production of high-purity hydrogen. This study employed three machine learning (ML) algorithms, random forest (RF), support vector machine (SVM), and eXtreme gradient boosting (XGBoost), to predict hydrogen production in PEMWE. Model performance was evaluated using root mean squared error (RMSE), coefficient of determination (R²), and mean absolute error (MAE) metrics. The top-performing models, RF and XGBoost, were further refined through hyperparameter tuning. The final models demonstrated high reliability in predicting hydrogen production rates, with RF consistently outperforming XGBoost. The RF model achieved a predictive accuracy of R² = 0.9898, RMSE = 19.99 mL/min, and MAE = 10.41 mL/min, while the XGBoost model achieved R² = 0.9894, RMSE = 20.43 mL/min, and MAE = 11.50 mL/min. Partial dependency plots (PDPs) emphasized the critical role of optimizing both cell voltage and current to maximize hydrogen production in PEMWE. These insights provide valuable guidance for operational adjustments, ensuring optimal system performance for high efficiency and productivity. The study suggests further research on the impact of parameters like temperature and power density on hydrogen production, incorporating them for better optimization.