An integrated EDIB model for probabilistic risk analysis of natural gas pipeline leakage accidents

• Published in: Journal of loss prevention in the process industries Vol. 83; p. 105027
• Main Authors: Chen, Xing-lin, Lin, Wei-dong, Liu, Chun-xiang, Yang, Fu-qiang, Guo, Yong, Li, Xin, Yuan, Shuai-qi, Reniers, Genserik
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.07.2023
• Subjects: Accident evolution analysis; Bayesian network; Gas leakage; Natural gas pipeline; Probabilistic risk analysis; Probabilistic risk analysis; Accident evolution analysis; Natural gas pipeline; Gas leakage; Bayesian network
• ISSN: 0950-4230
• DOI: 10.1016/j.jlp.2023.105027

Natural gas pipeline construction is developing rapidly worldwide to meet the needs of international and domestic energy transportation. Meanwhile, leakage accidents occur to natural gas pipelines frequently due to mechanical failure, personal operation errors, etc., and induce huge economic property loss, environmental damages, and even casualties. However, few models have been developed to describe the evolution process of natural gas pipeline leakage accidents (NGPLA) and assess their corresponding consequences and influencing factors quantitatively. Therefore, this study aims to propose a comprehensive risk analysis model, named EDIB (ET-DEMATEL-ISM-BN) model, which can be employed to analyze the accident evolution process of NGPLA and conduct probabilistic risk assessments of NGPLA with the consideration of multiple influencing factors. In the proposed integrated model, event tree analysis (ET) is employed to analyze the evolution process of NGPLA before the influencing factors of accident evolution can be identified with the help of accident reports. Then, the combination of DEMATEL (Decision-making Trial and Evaluation Laboratory) and ISM (Interpretative Structural Modeling) is used to determine the relationship among accident evolution events of NGPLA and obtain a hierarchical network, which can be employed to support the construction of a Bayesian network (BN) model. The prior conditional probabilities of the BN model were determined based on the data analysis of 773 accident reports or expert judgment with the help of the Dempster-Shafer evidence theory. Finally, the developed BN model was used to conduct accident evolution scenario analysis and influencing factor sensitivity analysis with respect to secondary accidents (fire, vapor cloud explosion, and asphyxia or poisoning). The results show that ignition is the most critical influencing factor leading to secondary accidents. The occurrence time and occurrence location of NGPLA mainly affect the efficiency of emergency response and further influence the accident consequence. Meanwhile, the weight ranking of economic loss, environmental influence, and casualties on social influence is determined with respect to NGPLAs. •This paper proposes a model for analyzing the natural gas pipeline leakage accidents.•This model can establish an accident environment similar to the actual situation and focuses on the analysis of key factor.•Diagnostic reasoning to determine the weights of evaluation indicators.•A combined multi-method model can comprehensively describe the evolution of an accident and predict its consequences.