Risk Analysis on Leakage Failure of Natural Gas Pipelines by Fuzzy Bayesian Network with a Bow-Tie Model

Abstract

Pipeline is the major mode of natural gas transportation. Leakage of natural gas pipelines may cause explosions and fires, resulting in casualties, environmental damage, and material loss. Efficient risk analysis is of great significance for preventing and mitigating such potential accidents. The objective of this study is to present a practical risk assessment method based on Bow-tie model and Bayesian network for risk analysis of natural gas pipeline leakage. Firstly, identify the potential risk factors and consequences of the failure. Then construct the Bow-tie model, use the quantitative analysis of Bayesian network to find the weak links in the system, and make a prediction of the control measures to reduce the rate of the accident. In order to deal with the uncertainty existing in the determination of the probability of basic events, fuzzy logic method is used. Results of a case study show that the most likely causes of natural gas pipeline leakage occurrence are parties ignore signage, implicit signage, overload, and design defect of auxiliaries. Once the leakage occurs, it is most likely to result in fire and explosion. Corresponding measures taken on time will reduce the disaster degree of accidents to the least extent.

Sand casting safety assessment for foundry enterprises: fault tree analysis, Heinrich accident triangle, HAZOP–LOPA, bow tie model

“…The bow tie model integrates the basic causes, possible effects and corresponding safety measures related to a given accident in a transparent diagram [28][29][30]. The bow tie model has been applied to safety assessment in natural gas pipelines [31,32] and ore mines as well [28,33].…”

System-level prognosis and health monitoring modeling framework and software implementation for gas pipeline system integrity management

“…The probabilities of basic events "Natural Force, "Third-Party Damage", "Material Defect", and "Welding Defect" were taken from the research work presented in the literature (Shan et al, 2017) assuming they are time-independent. Due to the lack of empirical input data information in the form of probability distributions, these values were regarded as median values of Lognormal distributions with error factors of 5 to quantify the uncertainties as shown in Table 8.…”

Reliability evaluation of dynamic face recognition systems based on improved Fuzzy Dynamic Bayesian Network

“…The fuzzy Bayesian network and the Fuzzy Dynamic Bayesian Network can solve the kind of problems. The former is a model that combines the fuzzy set theory and the simple Bayesian network, [22][23][24][25][26][27][28][29] which is premised on the basis of the hypothesis that the system reliability state is independent of time. The latter is a model that combines the fuzzy set theory and the dynamic Bayesian network, [30][31][32] which is premised on the basis of the hypothesis that the system reliability state is related to time and changes with time.…”