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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Na Yeon An & Jung Hyun Yang & Eunyong Song & Sung-Ho Hwang & Hyung-Gi Byun & Sanguk Park, 2024. "Digital Twin-Based Hydrogen Refueling Station (HRS) Safety Model: CNN-Based Decision-Making and 3D Simulation," Sustainability, MDPI, vol. 16(21), pages 1-26, October.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:21:p:9482-:d:1511433
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    References listed on IDEAS

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    1. Jia, Chunchun & Zhou, Jiaming & He, Hongwen & Li, Jianwei & Wei, Zhongbao & Li, Kunang, 2024. "Health-conscious deep reinforcement learning energy management for fuel cell buses integrating environmental and look-ahead road information," Energy, Elsevier, vol. 290(C).
    2. Usman, Muhammad R., 2022. "Hydrogen storage methods: Review and current status," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    3. M. R. Mahendrini Fernando Ariyachandra & Gayan Wedawatta, 2023. "Digital Twin Smart Cities for Disaster Risk Management: A Review of Evolving Concepts," Sustainability, MDPI, vol. 15(15), pages 1-25, August.
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