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A leakage risk assessment method for hazardous liquid pipeline based on Markov chain Monte Carlo

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  • Li, Zhengbing
  • Feng, Huixia
  • Liang, Yongtu
  • Xu, Ning
  • Nie, Siming
  • Zhang, Haoran

Abstract

Pipeline is now a commonly-used transportation mode for hazardous liquid, whereas followed by frequent pipeline leakage accidents. Research on the leakage post-assessment of hazard liquid pipeline has received increasing attention, but the in-situ data are hard to be accurately captured, resulting in a series of uncertainties affecting the accuracy and practicality of the risk assessment. This paper puts forward a novel method, which is able to accurately achieve leakage detection, cause analysis and leakage volume forecast by avoiding deviation of in-situ data, model parameters and the uncertainties caused by method, thereby quickly assessing the impact on the surrounding environment. The Markov Chain Monte Carlo (MCMC) algorithm is employed for repeatedly sampling leakage position and coefficient, furthermore, the transient hydrothermal and the leakage risk assessment model are established for determining the leakage volume and the risk grade. The influence of real-time measurement data and the deviation of the method are taken into consideration through the frequency distribution statistics of numerous sampling data. Based on two real examples, it is verified that the risk assessment method has practical value for the in-situ analysis and emergency treatment.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:ijocip:v:27:y:2019:i:c:s1874548219301222
    DOI: 10.1016/j.ijcip.2019.100325
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    Cited by:

    1. Zheng, Jianqin & Dai, Yuanhao & Liang, Yongtu & Liao, Qi & Zhang, Haoran, 2020. "An online real-time estimation tool of leakage parameters for hazardous liquid pipelines," International Journal of Critical Infrastructure Protection, Elsevier, vol. 31(C).
    2. 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.
    3. Zheng, Jianqin & Wang, Chang & Liang, Yongtu & Liao, Qi & Li, Zhuochao & Wang, Bohong, 2022. "Deeppipe: A deep-learning method for anomaly detection of multi-product pipelines," Energy, Elsevier, vol. 259(C).

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