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Third-Party Damage Model of a Natural Gas Pipeline Based on a Bayesian Network

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Listed:
  • Baikang Zhu

    (National & Local Joint Engineering Research Center of Harbor Oil & Gas Storage and Transportation Technology, Zhejiang Key Laboratory of Petrochemical Environmental Pollution Control, School of Petrochemical Engineering & Environment, Zhejiang Ocean University, Zhoushan 316022, China)

  • Xu Yang

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

  • Jun Wang

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

  • Chuanhui Shao

    (Zhejiang Zheneng Natural Gas Operation Co., Ltd., Hangzhou 310052, China)

  • Fei Li

    (Sinopec Sales Co., Ltd. Zhejiang Quzhou Petroleum Branch, Quzhou 324000, China)

  • Bingyuan Hong

    (National & Local Joint Engineering Research Center of Harbor Oil & Gas Storage and Transportation Technology, Zhejiang Key Laboratory of Petrochemical Environmental Pollution Control, School of Petrochemical Engineering & Environment, Zhejiang Ocean University, Zhoushan 316022, China
    Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China University of Petroleum-Beijing, Fuxue Road No. 18, Changping District, Beijing 102249, China)

  • Debin Song

    (National & Local Joint Engineering Research Center of Harbor Oil & Gas Storage and Transportation Technology, Zhejiang Key Laboratory of Petrochemical Environmental Pollution Control, School of Petrochemical Engineering & Environment, Zhejiang Ocean University, Zhoushan 316022, China)

  • Jian Guo

    (National & Local Joint Engineering Research Center of Harbor Oil & Gas Storage and Transportation Technology, Zhejiang Key Laboratory of Petrochemical Environmental Pollution Control, School of Petrochemical Engineering & Environment, Zhejiang Ocean University, Zhoushan 316022, China)

Abstract

Natural gas plays an important role in the transition from fossil fuels to new energy sources. With the expansion of pipeline networks, there are also problems with the safety of pipeline network operations in the process of transportation. Among them, third-party damage is a key factor affecting the safety of pipelines. In this paper, the risk factors of third-party damage are analyzed, and an evaluation model of natural gas pipeline damage is established using the GeNIe Modeler. Through Bayesian network reverse reasoning and a maximum cause chain analysis from the four aspects of personnel, environment, management, and equipment, it was found that the top five factors that have significant influence on third-party damage, are safety investment, the completeness of equipment, safety inspection frequency, the management of residents along the pipeline, and safety performance, with the posteriori probability in the model of 97.3%, 95.4%, 95.2%, 95.1%, 95.1%, respectively. Consequently, it is necessary for pipeline operation companies to secure investment on safety, to make sure that the safety equipment (system) works and is in a good condition, to maintain the safety inspection frequency in an organization, to build a management system for residents along the pipeline, and to conduct routine safety performance assessments accordingly.

Suggested Citation

  • Baikang Zhu & Xu Yang & Jun Wang & Chuanhui Shao & Fei Li & Bingyuan Hong & Debin Song & Jian Guo, 2022. "Third-Party Damage Model of a Natural Gas Pipeline Based on a Bayesian Network," Energies, MDPI, vol. 15(16), pages 1-12, August.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:16:p:6067-:d:894077
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    References listed on IDEAS

    as
    1. Khakzad, Nima & Khan, Faisal & Amyotte, Paul, 2011. "Safety analysis in process facilities: Comparison of fault tree and Bayesian network approaches," Reliability Engineering and System Safety, Elsevier, vol. 96(8), pages 925-932.
    2. Li, Zhengbing & Feng, Huixia & Liang, Yongtu & Xu, Ning & Nie, Siming & Zhang, Haoran, 2019. "A leakage risk assessment method for hazardous liquid pipeline based on Markov chain Monte Carlo," International Journal of Critical Infrastructure Protection, Elsevier, vol. 27(C).
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    Cited by:

    1. Chen, Xing-lin & Huang, Zong-hou & Ge, Fan-liang & Lin, Wei-dong & Yang, Fu-qiang, 2024. "A probabilistic analysis method for evaluating the safety & resilience of urban gas pipeline network," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
    2. Vadim Fetisov & Aleksey V. Shalygin & Svetlana A. Modestova & Vladimir K. Tyan & Changjin Shao, 2022. "Development of a Numerical Method for Calculating a Gas Supply System during a Period of Change in Thermal Loads," Energies, MDPI, vol. 16(1), pages 1-16, December.

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