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Risk Management of Island Petrochemical Park: Accident Early Warning Model Based on Artificial Neural Network

Author

Listed:
  • Guiliang Li

    (National-Local Joint Engineering Laboratory of Harbor Oil & Gas Storage and Transportation Technology/Zhejiang Provincial Key Laboratory of Petrochemical Pollution Control, Zhejiang Ocean University, Zhoushan 316022, China)

  • Bingyuan Hong

    (National-Local Joint Engineering Laboratory of Harbor Oil & Gas Storage and Transportation Technology/Zhejiang Provincial Key Laboratory of Petrochemical Pollution Control, Zhejiang Ocean University, Zhoushan 316022, China)

  • Haoran Hu

    (School of Naval Architecture and Maritime, Zhejiang Ocean University, Zhoushan 316022, China)

  • Bowen Shao

    (School of Naval Architecture and Maritime, Zhejiang Ocean University, Zhoushan 316022, China)

  • Wei Jiang

    (National-Local Joint Engineering Laboratory of Harbor Oil & Gas Storage and Transportation Technology/Zhejiang Provincial Key Laboratory of Petrochemical Pollution Control, Zhejiang Ocean University, Zhoushan 316022, China)

  • Cuicui Li

    (National-Local Joint Engineering Laboratory of Harbor Oil & Gas Storage and Transportation Technology/Zhejiang Provincial Key Laboratory of Petrochemical Pollution Control, Zhejiang Ocean University, Zhoushan 316022, China)

  • Jian Guo

    (National-Local Joint Engineering Laboratory of Harbor Oil & Gas Storage and Transportation Technology/Zhejiang Provincial Key Laboratory of Petrochemical Pollution Control, Zhejiang Ocean University, Zhoushan 316022, China)

Abstract

Island-type petrochemical parks have gradually become the ‘trend’ in establishing new parks because of the security advantages brought by their unique geographical locations. However, due to the frequent occurrence of natural disasters and difficulties in rescue in island-type parks, an early warning model is urgently needed to provide a basis for risk management. Previous research on early warning models of island-type parks seldom considered the particularity. In this study, the early warning indicator system is used as the input parameter to construct the early warning model of an island-type petrochemical park based on the back propagation (BP) neural network, and an actual island-type petrochemical park was used as a case to illustrate the model. Firstly, the safety influencing factors were screened by designing questionnaires and then an early warning indicator system was established. Secondly, particle swarm optimization (PSO) was introduced into the improved BP neural network to optimize the initial weights and thresholds of the neural network. A total of 30 groups of petrochemical park data were taken as samples—26 groups as training samples and 4 groups as test samples. Moreover, the safety status of the petrochemical park was set as the output parameter of the neural network. The comparative analysis shows that the optimized neural network is far superior to the unoptimized neural network in evaluation indicators. Finally, the Zhejiang Petrochemical Co., Ltd., park was used as a case to verify the accuracy of the proposed early warning model. Ultimately, the final output result was 0.8324, which indicates that the safety status of the case park was “safer”. The results show that the BP neural network introduced with PSO can effectively realize early warning, which is an effective model to realize the safety early warning of island-type petrochemical parks.

Suggested Citation

  • Guiliang Li & Bingyuan Hong & Haoran Hu & Bowen Shao & Wei Jiang & Cuicui Li & Jian Guo, 2022. "Risk Management of Island Petrochemical Park: Accident Early Warning Model Based on Artificial Neural Network," Energies, MDPI, vol. 15(9), pages 1-13, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:3278-:d:806114
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    References listed on IDEAS

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    Cited by:

    1. Bingang Guo & Jianfeng Gao & Bin Hao & Bingjian Ai & Bingyuan Hong & Xinsheng Jiang, 2022. "Experimental and Numerical Study on the Explosion Dynamics of the Non-Uniform Liquefied Petroleum Gas and Air Mixture in a Channel with Mixed Obstacles," Energies, MDPI, vol. 15(21), pages 1-16, October.

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