Performance prediction of experimental PEM electrolyzer using machine learning algorithms

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.