Machine Learning Applications in Electrolyzer Systems: A Comprehensive Review of Process Control, Predictive Maintenance, and Optimization Strategies

Abstract

Artificial intelligence (AI) has emerged as a transformative technology capable of addressing complex engineering challenges through advanced data-driven modeling, prediction, and optimization techniques. In the context of sustainable energy systems, hydrogen production via water electrolysis has attracted considerable attention as a promising pathway toward carbon-neutral energy generation. However, the widespread deployment of electrolyzer technologies remains constrained by challenges related to energy efficiency, operational stability, system degradation, and dynamic process control. In response, machine learning (ML) techniques have increasingly been integrated into electrolyzer systems to enhance performance prediction, adaptive control, predictive maintenance, and operational optimization. This review presents a comprehensive and engineering-oriented analysis of recent advancements in ML applications for hydrogen production via electrolysis. The study first outlines the fundamental principles of ML, including major learning paradigms, data processing approaches, and commonly adopted algorithms for electrochemical systems. Subsequently, the review critically examines ML-driven strategies for hydrogen production optimization, hyperparameter tuning, intelligent process control, system design enhancement, and degradation monitoring in electrolyzer technologies such as Proton Exchange Membrane Water Electrolyzers (PEMWE) and Anion Exchange Membrane Water Electrolyzers (AEMWE). Comparative analysis of algorithms including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Reinforcement Learning (RL), and Physics-Informed Neural Networks (PINN) is also discussed from both electrochemical and engineering perspectives. Furthermore, current challenges involving data quality, model interpretability, computational complexity, and industrial deployment are critically evaluated. Overall, this review provides strategic insights into future intelligent electrolyzer systems and highlights critical research directions for scalable and sustainable green hydrogen production.