TY  - EJOU
AU  - Hasulyó, Gábor 

TI  - Dynamic Digital Twin Network for Real-Time Safety Monitoring and Predictive Risk Assessment of Hydrogen Refueling Infrastructure
T2  - Energy Engineering

PY  - 
VL  - 
IS  - 
SN  - 1546-0118

AB  - The current global energy situation is very fragile. Much more stable and predictable energy security is needed. Due to global climate conditions, it is advisable to prioritize fuels that are high in energy content and relatively easy to produce, such as hydrogen. However, the widespread deployment of hydrogen refueling stations is hampered by significant safety challenges, including hydrogen’s high flammability, its tendency to leak, and high-pressure storage requirements. This study examines how digital twin technology can be implemented to improve the safety and operational efficiency of hydrogen facilities. A dynamic digital twin model was developed using the MATLAB/Simulink environment, replicating critical components such as the compressor, high-pressure buffer tank, heat exchanger, cooler, and dispenser. The methodology involved integrating real-time data streams with virtual models and validating the simulation with operational data from an existing Hungarian refueling station. The results show that the digital twin system accurately tracks critical parameters, with the vehicle tank temperature reaching 61.5°C and the storage pressure rising to 955 bar, both within the safe operating range. The simulation confirmed that the system provides effective real-time leak detection, automatic alarms and accurate mass flow measurement, delivering 6.2 kg of hydrogen during a 300-s refueling period. In conclusion, digital twin technology offers significant potential for the safe development of hydrogen infrastructure by enabling predictive maintenance and optimized energy consumption. While challenges such as big data and infrastructure costs remain, the integration of artificial intelligence and advanced sensors into the digital twin will play a key role in the transition to a sustainable and reliable hydrogen economy.
KW  - Artificial intelligence; dynamic simulation; hydrogen refueling station; infrastructure safety; thermodynamic modeling

DO  - 10.32604/ee.2026.081099