Digital Twin-Based Hydrogen Refueling Station (HRS) Safety Model: CNN-Based Decision-Making and 3D Simulation

Na Yeon An, Jung Hyun Yang, Eunyong Song, Sung-Ho Hwang, Hyung-Gi Byun and Sanguk Park ()
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Na Yeon An: Department of Electronic, Information and Communication Engineering, Kangwon National University, Samcheok 25913, Republic of Korea
Jung Hyun Yang: Department of Electronic, Information and Communication Engineering, Kangwon National University, Samcheok 25913, Republic of Korea
Eunyong Song: Department of Electronic, Information and Communication Engineering, Kangwon National University, Samcheok 25913, Republic of Korea
Sung-Ho Hwang: Department of Electronic, Information and Communication Engineering, Kangwon National University, Samcheok 25913, Republic of Korea
Hyung-Gi Byun: Department of Electronic, Information and Communication Engineering, Kangwon National University, Samcheok 25913, Republic of Korea
Sanguk Park: Department of Electronic, Information and Communication Engineering, Kangwon National University, Samcheok 25913, Republic of Korea

Sustainability, 2024, vol. 16, issue 21, 1-26

Abstract: This study presents a safety management model for hydrogen refueling stations, integrating digital twin technology and artificial intelligence (AI) to enhance operational safety. Given the risks associated with high-pressure gas handling and potential fires from hydrogen leaks, real-time safety monitoring is crucial. The proposed model is based on a digital twin, a virtual replica of the physical system using real-time data, including temperature, pressure, and state of charge, collected from an actual hydrogen refueling station in Samcheok, Gangwon Province. Out of nine tested machine learning and deep learning algorithms, the convolutional neural network (CNN) demonstrated the highest performance (accuracy: 1, F1 score: 0.993) for risk prediction. Using AI libraries like Scikit-Learn and TensorFlow, the model achieved prediction times of 68 milliseconds, enabling decision-making at intervals of 1 s. Developed with the Unity 3D modeling tool, the digital twin visualizes predicted risk situations, allowing users to quickly identify and respond to potential hazards. This approach offers a robust solution for improving the safety of hydrogen refueling stations.

Keywords: digital twin; hydrogen refueling station; prediction model; machine learning; deep learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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