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Improved quantitative risk assessment of a natural gas pipeline considering high-consequence areas

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Listed:
  • Yin, Yuanbo
  • Yang, Hao
  • Duan, Pengfei
  • Li, Luling
  • Zio, Enrico
  • Liu, Cuiwei
  • Li, Yuxing

Abstract

Currently, quantitative risk assessment is often used for pipeline safety analysis. However, the low probability of pipeline failure may produce a false safety evaluation result because high-consequence areas with high population densities cannot be effectively identified. Furthermore, nearly half of the gas leakage accidents in China have occurred in densely populated areas. Therefore, an improved quantitative risk assessment method is proposed. First, we establish two models: (1) a failure probability model based on improved historical failure and disaster derivation probabilities and (2) a risk consequence model considering potential direct and indirect losses based on the probability of disaster evolution. Considering the concept of human rights equality and the social model of “life is first†in China, a method to correct the loss value of life according to population density is proposed, which can effectively avoid the hidden phenomenon of high-consequence areas mentioned above. A pipeline in China is evaluated using this improved method. Compared with traditional evaluation results, the new evaluation method can effectively identify a high-consequence area and obtain more reasonable results. Thus, a pipeline maintenance plan can ensure the interests of enterprises and fully respect the lives of individuals threatened by the potential risk of the pipeline.

Suggested Citation

  • Yin, Yuanbo & Yang, Hao & Duan, Pengfei & Li, Luling & Zio, Enrico & Liu, Cuiwei & Li, Yuxing, 2022. "Improved quantitative risk assessment of a natural gas pipeline considering high-consequence areas," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    • Handle: RePEc:eee:reensy:v:225:y:2022:i:c:s0951832022002290
    DOI: 10.1016/j.ress.2022.108583
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    3. Xiao, Rui & Zayed, Tarek & Meguid, Mohamed A. & Sushama, Laxmi, 2024. "Improving failure modeling for gas transmission pipelines: A survival analysis and machine learning integrated approach," Reliability Engineering and System Safety, Elsevier, vol. 241(C).

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