Machine-Learning-Driven Operational Optimisation of Electrolyser-Based Hydrogen Production Using a Physics-Informed Digital Twin for UK Industrial Clusters

The article investigates the optimization of the process of producing hydrogen by electrolyser through machine learning (ML) and physics-informed digital twin (DT) technologies to increase efficiency and decrease costs in industrial sectors in the United Kingdom. The study utilizes simulation-based assessment to predict the performance of the system and optimize the working parameters through a digital twin that replicates the behavior of the electrolyser in real-time and allows adapting optimization. The methodology involves collecting a PEM the electrolyser system's operating parameters and using machine learning models to forecast system performance metrics like energy consumption and hydrogen production rate. A digital twin with physics knowledge is created to mimic the behavior of the electrolyser under various operating circumstances. Through simulation experiments, the proposed MLDT framework is evaluated by comparing optimized operation with a conventional baseline system. The system was tested within the framework of the UK industrial clusters, and the specific were decarbonization and the energy efficiency. The main results based on simulations are the decrease in energy usage by 15−20%, downtime and maintenance reduction by 25−35%, and better operational reliability. The suggested optimization scheme states strong possibilities to promote the development of sustainable hydrogen production and sustainability of the UK net-zero objectives.