Optimization monitoring distribution method for gas pipeline leakage detection in underground spaces. (October 2020)

• Record Type: Journal Article
• Authors: Zhang, Zewei; Hou, Longfei; Yuan, Mengqi; Fu, Ming; Qian, Xinming; Duanmu, Weike; Li, Yuanzhi
• Is Part Of: Tunnelling and underground space technology. Volume 104(2020)
• Publication Date: 2020-10
• Subjects: Natural gas pipeline -- Gas leakage monitoring -- Underground space -- Measurement optimization -- Fire and explosion risk; Tunneling -- Periodicals; Underground construction -- Periodicals; Tunnels -- Periodicals; Underground areas -- Periodicals; 624.193
• DOI: 10.1016/j.tust.2020.103545
• Languages: English
• ISSNs: 0886-7798

Abstract

Highlights: An optimized distribution model for gas pipeline leakage detection was developed. Total risk of underground space system was calculated using the micro-element method. Relationship between the number of monitoring points and total risk could be obtained. The rate of risk control of the proposed model is twice that of the conventional model. Abstract: Gas leakage from buried gas pipelines in urban areas can lead to accidents involving fire and explosion when the gas gets concentrated into the adjacent underground spaces. Determining monitoring points for the gas leakage in the underground spaces can prevent the initiation of fire and explosion. In this regard, this study proposes an optimized distribution model which relies on risk prediction. It maps the fire and explosion risk in the underground spaces to the discrete target pipeline based on the effect predicted by the monitoring sensors. Moreover, the total risk in this system is calculated through the micro-element method to design an effective distribution optimization strategy. A case study is conducted to illustrate the effectiveness of the new approach and compare it with the risk-based distribution method and effective monitoring length method. The results show that determining the optimized distribution plan is difficult using the risk-based distribution method and effective monitoring length method because these methods may determine a large number of monitoring points or cannot determine the specific location of the monitoring point. The proposed optimization model enables to derive the relationship between the number of distribution points and the risk in the system. For the same number of monitoring points, the rate of risk control in the system of the proposed model is twice that of the conventional model. As the number of monitoring points decreases, the monitoring cost for the prevention of fire and explosion would be largely reduced.