Enhancing predictive accuracy in alkaline water electrolysis: A machine learning approach to the effects of trans‐diaphragm fluid flow using experimental data

Abstract

Enhancing the efficiency of alkaline water electrolysis is critical for large‐scale green hydrogen production, yet accurately predicting hydrogen‐in‐oxygen concentrations remains a significant challenge due to the complex nonlinear interactions between electrochemical and fluid dynamic parameters. This study employs machine learning to improve the predictive accuracy of hydrogen‐in‐oxygen levels under varying trans‐diaphragm fluid flow conditions, addressing a gap in existing modelling approaches that rely primarily on theoretical or empirical methods. Five artificial neural network models were developed using experimental data from a 0.6 m single‐stack electrolyzer operating with an electrolyte. The models were trained and tested on 132 experimental data points, with 75% allocated for training and 25% for testing. The number of neurons in the hidden layer of the network models developed with a single hidden layer and the TanSig activation function was optimized by analyzing the performance of different network models. The models achieved exceptional predictive accuracy, with mean squared errors below 1.47E‐02, correlation coefficients exceeding 0.989, and margin of deviation within ±0.82% across all test cases. These findings confirm the capability of machine learning‐based predictive modelling to enhance electrolysis optimization, reduce experimental costs, and support the scalable deployment of green hydrogen production. The novel integration of machine learning in trans‐diaphragm fluid flow analysis advances predictive modelling beyond conventional techniques, offering a robust approach for industrial‐scale electrolysis system enhancement. This study primarily aims to develop accurate predictive models for hydrogen‐in‐oxygen concentrations under varying trans‐diaphragm flow conditions, addressing a critical gap in monitoring and controlling alkaline water electrolysis systems.