Intelligent Digital Twin Framework for Real-Time Monitoring and Performance Optimization of Green Hydrogen Electrolyser Systems
Year: 2026
Type: conference-paper
Abstract: Real-time monitoring and adaptive optimization of green hydrogen electrolyser systems are critical for ensuring maximum efficiency, stability, and consistent hydrogen production when renewable energy inputs fluctuate. In this paper, we introduce a novel digital twin based intelligent framework for green hydrogen electrolysers that combines a physics-based electrolyser model, real-time sensor data, artificial intelligence-based prediction, and optimization driven decision support. The digital twin continuously synchronizes the physical electrolyser and the virtual electrolyser to predict current operating states and detect when performance deviates from those predicted, allowing the operator to modify operating conditions to optimize performance. Based on the scientific literature, we established a connection between temperature and the output of current density, how much hydrogen is produced, how much power is consumed, and how efficient the unit is operating, indicating the necessity of using a more sophisticated means of operating electrolysers. Our intelligent framework provides a practical means of enhancing efficiency, increasing reliability, and providing a digitalized means to operate electrolysers for producing green hydrogen.
Source: EPJ Web of Conferences
Language: English
Subfield: Energy Engineering and Power Technology
Field: Energy
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7203739880