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Simulation of a Hazardous Chemical Cascading Accident Using the Graph Neural Network

Author

Listed:
  • Wenqi Cui

    (Experimental Teaching Center, Hubei University of Economics, Wuhan 430205, China)

  • Xinwu Chen

    (Experimental Teaching Center, Hubei University of Economics, Wuhan 430205, China)

  • Weisong Li

    (Experimental Teaching Center, Hubei University of Economics, Wuhan 430205, China)

  • Kunjing Li

    (Experimental Teaching Center, Hubei University of Economics, Wuhan 430205, China)

  • Kaiwen Liu

    (Experimental Teaching Center, Hubei University of Economics, Wuhan 430205, China)

  • Zhanyun Feng

    (School of Resource and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China)

  • Jiale Chen

    (School of Resource and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China)

  • Yueling Tian

    (School of Resource and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China)

  • Boyu Chen

    (Wuhan Ulink College of China Optics Valley, Wuhan 430205, China)

  • Xianfeng Chen

    (School of Safety Science and Emergency Managent, Wuhan University of Technology, Wuhan 430070, China)

  • Wei Cui

    (School of Resource and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China)

Abstract

In the storage of hazardous chemicals, due to space limitations, various hazardous chemicals are usually mixed stored when their chemical properties do not conflict. In a fire or other accidents during storage, the emergency response includes two key steps: first, using fire extinguishers like dry powder and carbon dioxide to extinguish the burning hazardous chemicals. In addition, hazardous chemicals around the accident site are often watered to cool down to prevent the spread of the fire. But both the water and extinguishers may react chemically with hazardous chemicals at the accident site, potentially triggering secondary accidents. However, the existing research about hazardous chemical domino accidents only focuses on the pre-rescue stage and ignores the simulation of rescue-induced accidents that occur after rescue. Aiming at the problem, a quantitative representation algorithm for the spatial correlation of hazardous chemicals is first proposed to enhance the understanding of their spatial relationships. Subsequently, a graph neural network is introduced to simulate the evolution process of hazardous chemical cascade accidents. By aggregating the physical and chemical characteristics, the initial accident information of nodes, and bi-temporal node status information, deep learning models have gained the ability to accurately predict node states, thereby improving the intelligent simulation of hazardous chemical accidents. The experimental results validated the effectiveness of the method.

Suggested Citation

  • Wenqi Cui & Xinwu Chen & Weisong Li & Kunjing Li & Kaiwen Liu & Zhanyun Feng & Jiale Chen & Yueling Tian & Boyu Chen & Xianfeng Chen & Wei Cui, 2024. "Simulation of a Hazardous Chemical Cascading Accident Using the Graph Neural Network," Sustainability, MDPI, vol. 16(18), pages 1-20, September.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:18:p:7880-:d:1474750
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    References listed on IDEAS

    as
    1. Li, Xin & Chen, Chao & Hong, Yi-du & Yang, Fu-qiang, 2023. "Exploring hazardous chemical explosion accidents with association rules and Bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
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