Integrating Digital Twins and Machine Learning for Advanced Control in Green Hydrogen Production

The successful reduction of carbon emissions in major sectors such as heavy industry and long-distance transport depends crucially on the ability to produce green hydrogen on a large scale. This involves generating hydrogen via water electrolysis, utilizing power sourced from renewable energies. However, persistent challenges, such as dynamic inefficiencies, material degradation, and renewable intermittency, demand a paradigm shift from static control strategies to adaptive, self-optimizing systems. This perspective argues that the synergistic integration of digital twins (DTs) and machine learning (ML) offers a transformative framework for real-time optimization, predictive maintenance, and resilient grid integration. By synthesizing physics-based modeling with data-driven intelligence, DT-ML systems enable closed-loop control architectures that dynamically adapt to operational uncertainties. We analyze the technical foundations of this integration, address critical barriers, and propose actionable pathways for stakeholders to accelerate the hydrogen economy's transition from promise to practice.