Predictive analysis for risk of fire and explosion of LNG storage tanks by fuzzy Bayesian network

2019 ◽  
Vol 9 (3) ◽  
pp. 319-328
Author(s):  
Bilal Zerouali ◽  
Brahim Hamaidi
2019 ◽  
Vol 2019 ◽  
pp. 1-19
Author(s):  
Wen Liu ◽  
Shihong Zhai ◽  
Wenli Liu

A hybrid method consisting of bow-tie-Bayesian network (BT-BN) analysis and fuzzy theory is proposed in this research, in order to support predictive analysis of settlement risk during shield tunnel excavation. We verified the method by running a probabilistic safety assessment (PSA) for a tunnel section in the Wuhan metro system. Firstly, we defined the “normal excavation phase” based on the fuzzy statistical test theory. We eliminated the noise records in the tunnel construction log and extracted the occurrence probability of facility failures from the denoised database. We then obtained the occurrence probability of environmental failures, operational errors, and multiple failures via aggregation of weighted expert opinions. The expert opinions were collected in the form of fuzzy numbers, including triangular numbers and trapezoidal numbers. Afterwards, we performed the BT-BN analysis. We mapped the bow-tie analysis to the Bayesian network and built a causal network PSA model consisting of 16 nodes. Causes of the excessive surface settlement and the resulting surface collapse were determined by bow-tie analysis. The key nodes of accidents were determined by introducing three key measures into the Bayesian inference. Finally, we described the safety measures for the key nodes based on the PSA results. These safety measures were capable of reducing the failure occurrence probability (in one year) of excessive surface settlement by 66%, thus lowering the accident probability caused by excessive surface settlement.


2021 ◽  
Vol 261 ◽  
pp. 03055
Author(s):  
Kezhen Chen ◽  
Jihong Ye ◽  
Xiaofeng Zhang ◽  
Qingqing Lv

In order to explore the basic events and risk occurrence probability of fire and explosion accidents in CNG (Compressed Natural Gas) filling station, a corresponding Bayesian network risk model was established based on the fault tree of filling station. The prior probability was modified by introducing fuzzy mathematics in the process of transforming the fault tree into Bayesian network, and the posterior probability of the basic events of CNG filling station fire and explosion accidents was analyzed and calculated by GeNIe software. Finally, through case analysis, it is found out that the most dangerous factors that lead to the greatest risk of fire and explosion accidents in a filling station are: personnel misoperation, management defects, etc. After verifying the model, it shows that paying attention to the polymorphism of the base events and determining the rationality of the logical relationship between the base events can calculate the more accurate probability distribution of the base events, and at the same time provide reasonable suggestions for the accident prevention of the gas filling station.


2016 ◽  
Vol 36 (2) ◽  
pp. 202-212 ◽  
Author(s):  
Zerouali Bilal ◽  
Kara Mohammed ◽  
Hamaidi Brahim

2021 ◽  
Vol 18 (1) ◽  
pp. 139-154
Author(s):  
Tahere eskandari ◽  
Mostafa Mirzaei ◽  
Iraj mohammadfam ◽  
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2020 ◽  
Vol 198 ◽  
pp. 01021
Author(s):  
Zhenping Li ◽  
Sanming Wang ◽  
Dongliang Sun

The placement of chemical storage tanks is an important topic in industrial safety, and its placement method is based on the study of the safety spacing of storage tanks. This paper takes LPG and LNG storage tanks as examples. It uses vapor cloud explosions, pool fires, pressure vessel explosions, boiling liquid expansion vapor explosions and other fire and explosion accident consequences models and risk probability analysis methods to analyze. It is proved that the transfer of storage tanks from ground to underground can significantly reduce the scope of impact of explosion accidents, thereby increasing the utilization rate of industrial land.


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