基于 MCMC 算法的天然气管道泄漏检测方法

[Objective] With the implementation of the “dual carbon” strategic goals, China is experiencing a rapid increase in natural gas demand. However, the occurrence of leakage accidents in pipelines carrying this flammable and explosive gas poses potential threats, including casualties, environmental pollution, and economic losses. As a result, the research on detecting leaks in natural gas pipelines becomes particularly significant. [Methods] Utilizing the Gaussian plume model and Unmanned Aerial Vehicles (UAVs) equipped with methane concentration sensors, this study employed the Markov Chain Monte Carlo (MCMC) method based on Bayesian inference to determine the positions and rates of leakage sources along natural gas pipelines. The inverse computation method of gas source intensity, relying on probability statistics, was employed to calculate leakage parameter intervals with the highest probability. To verify the effectiveness of the MCMC algorithm in identifying leakage sources in natural gas pipelines, gas leakage simulations were conducted in a scenario featuring continuous leakage accidents of overhead natural gas pipelines. [Results] The MCMC algorithm demonstrated its effectiveness in calculating the positions and rates of leakage in natural gas pipelines. However, its success rate declined due to increasing overall errors. Nonetheless, data cleaning greatly enhanced the algorithm’s ability to adapt to errors, with a success rate exceeding 90% when supported by data cleaning. In contrast, the success rates gradually declined without any data processing. By applying inverse computation of hazardous gas source intensity to leakage detection, more accurate results were obtained regarding the positions and rates of pipeline leakage. It was observed that when the initial points were distant from the actual leakage source, the algorithm’s performance diminished. Hence, a rational selection of the initial point proved beneficial for the overall operation of the algorithm. [Conclusion] The combined approach involving the MCMC algorithm and UAVs equipped with methane concentration sensors effectively enables the simultaneous identification of leakage positions and rates in natural gas pipelines. The findings of this study hold significant importance for facilitating emergency response measures to leakage accidents.