Performance prediction of experimental PEM electrolyzer using machine learning algorithms

• Published in: Fuel (Guildford) Vol. 378; p. 132853
• Main Authors: Ozdemir, Safiye Nur, Pektezel, Oguzhan
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.12.2024
• Subjects: Machine learning; Multilayer perceptron; PEM water electrolyzer; Random forest; Support vector machine; PEM water electrolyzer; CFD; LGDL; PEMWE; ANN; CD; MEA; Random forest; Support vector machine; SVM; RMSE; SOWE; AWE; MLP; MAE; DT; CTC; RF; HFR; Machine learning; Multilayer perceptron; ML; DC
• ISSN: 0016-2361
• DOI: 10.1016/j.fuel.2024.132853

•Investigated various ML algorithms to model PEM electrolyzer performance characteristics.•Employed MLP, SVM, and RF algorithms for predictions of current density and hydrogen flowrate.•The SVM model is the best technique for simulating performance evaluation with high accuracy.•The SVM algorithm produces an MAE of 0.0317 for current density and 0.0671 for hydrogen flowrate. Proton exchange membrane water electrolyzer (PEMWE) is a sustainable energy conversion device that uses electrical energy to oxidize water and convert it into chemical energy (hydrogen and oxygen) and heat. Despite their numerous advantages, such as high current density, efficiency, high-purity hydrogen production, compatibility with renewable energy sources, and compact design, they have not yet matured due to durability, cost, and electrochemical performance deficiencies. Effective assessment of performance and durability is crucial for electrolyzer design and optimization. This study analyzes the electrochemical performance of PEMWE with artificial intelligence approaches using experimental data. Three different machine learning (ML) techniques − Multilayer Perceptron (MLP), Support Vector Machine (SVM), and Random Forest (RF) were trained and tested for predicting the hydrogen flowrate and current density for PEMWE using different input parameters. These input parameters include cell voltage, temperature, torque, and water flowrate. SVM was detected to be the best technique in predicting all output parameters with a Mean Absolute Error (MAE) of 0.0317 and 0.0671 for current density and hydrogen flowrate predictions in the test set, respectively. The study results show that machine-learning algorithms can optimize operational parameters in electrolyzer control systems.