Study on quantitative risk assessment model of the third party damage for natural gas pipelines based on fuzzy comprehensive assessment

12th International Conference on Damage Assessment of Structures

Qiu, Zeyang^1 ; Liang, Wei^1 ; Wang, Xue^2 ; Lin, Yang^1 ; Zhang, Meng^1

College of Mechanical and Transportation Engineering, China University of Petroleum-Beijing, China^1

China Building Material Test and Certification Group Co., Ltd., China^2

关键词: Analytic hierarchy process (ahp);    Environmental pollutions;    Fuzzy comprehensive assessment;    Fuzzy comprehensive evaluation;    Fuzzy Comprehensive Evaluation(FCE);    Quantitative risk assessment;    Third party damage;    Transmission pipelines;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/842/1/012048/pdf
DOI  :  10.1088/1742-6596/842/1/012048

来源: IOP

【 摘 要 】

As an important part of national energy supply system, transmission pipelines for natural gas are possible to cause serious environmental pollution, life and property loss in case of accident. The third party damage is one of the most significant causes for natural gas pipeline system accidents, and it is very important to establish an effective quantitative risk assessment model of the third party damage for reducing the number of gas pipelines operation accidents. Against the third party damage accident has the characteristics such as diversity, complexity and uncertainty, this paper establishes a quantitative risk assessment model of the third party damage based on Analytic Hierarchy Process (AHP) and Fuzzy Comprehensive Evaluation (FCE). Firstly, risk sources of third party damage should be identified exactly, and the weight of factors could be determined via improved AHP, finally the importance of each factor is calculated by fuzzy comprehensive evaluation model. The results show that the quantitative risk assessment model is suitable for the third party damage of natural gas pipelines and improvement measures could be put forward to avoid accidents based on the importance of each factor.