Reducing energy use in industrial alkaline electrolyzers with machine learning

The use of industrial data collected during production shows potential to improve energy efficiency and operational stability in alkaline water electrolysis. In this study, we propose a data-driven framework for a one-megawatt industrial alkaline electrolyzer operated for over two years. Using high-frequency operational data, we train supervised machine learning models to predict the liquid-level imbalance between the hydrogen and oxygen gas separators ten seconds ahead. The models use process and electrical variables, together with causal temporal features that describe recent separator dynamics. Unlike many studies focused on stack-level indicators or offline optimization, this work targets a critical balance-of-plant subsystem. It is designed to support existing industrial control architectures without redesigning the baseline safety and control logic. The evaluation includes naive baselines, chronological validation, horizon sensitivity, latency, residual diagnostics, and peak-event analysis. Under chronological testing, random forest and linear regression reduced root mean square error by about 24% compared with the persistence baseline. Random forest achieved the lowest error. Linear regression provided nearly equivalent accuracy with lower latency and higher transparency. XGBoost remained a fast nonlinear alternative but was less robust under temporal distribution shifts. The results show that short-horizon separator imbalance prediction is feasible on real industrial data. The study provides a deployment-oriented framework with prediction validation, safe fallback, and human oversight.