Analysis and modeling of high-performance polymer electrolyte membrane electrolyzers by machine learning

Abstract

In this study, box and whisker and principal component analysis, as well as classification and regression tree modeling as a part of machine learning were performed on a database constructed on PEM (polymer electrolyte membrane) electrolysis with 789 data points from 30 recent publications. Box whisker plots discovered that pure Pt at the cathode surface, Ti at the anode support, the existence of Pt, Ir, Co, Ru at the anode surface, Ti porous structures at the electrodes, pure water-electrolyte and Nafion and Aquivion type membranes in proton exchange electrolyzer provide the highest performances. Principal component analysis indicated that when cathode surface consists of mostly pure Ni, when anode electrode has no support or vanadium (10–20%) doped TiO2 support and when anode electrode surface consists of cobalt-iron alloys (0.5:0.5 and 0.333:0.666 mol ratio) or RuO2, there is a risk for low-performance. Classification trees revealed that other than current density and potential, cathode surface Ni mole fraction, anode surface Co mole fraction are the most important variables for the performance of an electrolyzer. Finally, the regression tree technique successfully modeled the polarization behavior with a RMSE (root mean square error) value of 0.18.