Citation

Chen J, Wu Y, Li Y, Qian X, Yuan M. Trans. Beijing Inst. Technol. 2021; 41(7): 696-705.

Copyright

(Copyright © 2021, Beijing Institute of Technology)

DOI

10.15918/j.tbit1001-0645.2020.170

PMID

unavailable

Abstract

In view of the limitation that the traditional risk analysis method cannot realize dynamic assessment, a risk analysis model of the gas pipeline network based on dynamic Bayesian network was proposed. Based on the causes of pipeline failure and accident consequences, the network structure of the whole process of accident evolution was constructed, and the Markov theory was used to associate the whole process of evolution with time, and finally a risk analysis model based on the dynamic Bayesian network was established. The model calculates the probability of leakage, disaster mode and consequence node based on the Bayesian theory, and realizes the cause diagnosis and accident developing trend prediction of the gas pipeline network accident. Taking "7.4" gas explosion accident in Songyuan city as an example, this model is used to demonstrate the accident deduction technology of the gas pipeline network accident. The results show that the accident is resulted from gas leakage caused by the destruction of the third-party construction, and because the diffusion range of gas in the soil is constantly expanding, the number of restricted spaces in the diffusion range is increasing, and the situation of human activities is more complex, thus increasing the probability of gas accumulation and ignition. Combined with the conditions of the accident scene, the model deduces the probability of the explosion and concludes that the probability of the gas explosion will reach 42.3% after 60 minutes of leakage, which is higher than the safe release probability. The result is basically consistent with the actual situation, which further verifies the feasibility and reliability of the model. © 2021, Editorial Department of Transaction of Beijing Institute of Technology

Keywords: Pipeline transportation

Language: zh

Keywords

Accidents; Explosions; Risk assessment; Risk analysis; Pipelines; Gases; Bayesian networks