Research on Multi-Point Monitoring Data Grid Model and Inversion Positioning Method for Gas Leakage in Oil and Gas Stations

Ge Meng and Hao Hu ()
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Ge Meng: School of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan 430074, China
Hao Hu: School of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan 430074, China

Sustainability, 2024, vol. 16, issue 4, 1-24

Abstract: The safe and stable operation of oil and gas stations makes a positive contribution to the stability and reliability of the natural gas supply. In order to reduce the impact of leakage and diffusion accidents in the station, it is necessary to develop an effective method to monitor and locate the leakage source quickly and accurately. This study proposes a multi-point monitoring data grid model to achieve the full-coverage monitoring of oil and gas stations. In addition, on this basis, a leakage source inversion positioning model is established to realize the leakage positioning of the station. A field experiment was carried out with an oil and gas station as an example. The results show that the optimal layout of points needs to consider the influence of environmental factors, confirmed by computational fluid dynamics (CFD) simulation. The optimized data interpolation not only reduces the cost of the monitoring point layout. In addition, through the comparison of multi-objective optimization algorithms and a robustness test, it can be found that the convergence efficiency and accuracy of the inversion positioning algorithm in this study have been greatly improved. Compared with the manual auxiliary positioning method, this method effectively solves the problem of leakage monitoring and positioning of oil and gas stations, and can achieve the purpose of leakage risk monitoring and “reducing cost and increasing efficiency”.

Keywords: oil and gas stations; leakage diffusion; point optimization layout; multi-objective optimization algorithm; inverse positioning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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