From Digital Twin to AI-Integrated Control: A Review and Research Agenda for Large-Scale PEM Electrolyzer Plant Management

Large-scale green hydrogen production via PEM electrolysis demands control strategies that surpass the limitations of traditional distributed control systems (DCSs). Digital twin (DT) technology has been introduced as a structured design framework for predictive maintenance and operational optimization across hydrogen production pathways, from steam methane reforming and green ammonia to next-generation PEM electrolyzer. A critical constraint exists; passive DT architectures cannot autonomously close the control loop. The resulting prediction–action latency gap introduces delays of 28–120 min precisely when dynamic renewable energy loads require sub-second responses. This review makes three original contributions; it characterizes the prediction–action latency gap as a structural design constraint across SMR, green ammonia, and PEM electrolyzer DT deployments, based on a structured Scopus and Web of Science search; it proposes a three-tier DCS–digital twin–AI architecture as the solution; and it defines five purpose-designed AI algorithm modules—Stack State Estimator, Degradation Trajectory Predictor, Fleet Dispatcher, Anomaly and Fault Classifier, and Maintenance Scheduler—together with an eight-challenge research agenda with technology readiness level assessments. All projections are extrapolated from adjacent domains and require electrolyzer-specific experimental validation.