摘要
【目的】在“双碳”战略目标下,中国天然气消费需求正快速增长,但天然气具有易燃易爆性,一旦天然气管道发生泄漏事故,易造成人员伤亡、环境污染以及经济损失等,天然气管道泄漏检测的研究显得尤为重要。【方法】以高斯烟羽模型与加装甲烷浓度传感器的无人机为基础,采用基于贝叶斯推理的马尔科夫链蒙特卡洛(Markov Chain Monte Carlo,MCMC)法获取天然气管道泄漏源的泄漏位置、泄漏速率;采用基于概率统计的气体源强反算方法,计算概率最高的泄漏参数区间。利用设置的天然气架空管道连续泄漏事故场景进行气体泄漏模拟,验证MCMC算法确定天然气管道泄漏源的有效性。【结果】MCMC算法通过计算得到天然气管道泄漏位置和泄漏速率,总误差的增大使得MCMC算法的成功率降低,但数据清洗会增强算法误差适应性,未经过数据处理的算法成功率则逐渐降低,而经过数据清洗的算法成功率超过90%;将危险气体源强反算的思想应用于天然气管道泄漏检测中,有助于更加准确地获得管道泄漏位置与泄漏速率;初始点远离真实泄漏源会降低MCMC算法的性能,因此合理地选择初始点有利于算法的运行。【结论】基于MCMC算法与加装甲烷浓度传感器的无人机相结合的检测方法,可同时确定天然气管道的泄漏位置与泄漏速率,对泄漏事故发生后的应急处理具有重要意义。(图7,表5,参24)
[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.(7 Figures,5 Tables,24 References)
作者
赵锦鹏
周文静
白云龙
张永海
魏进家
ZHAO Jinpeng;ZHOU Wenjing;BAI Yunlong;ZHANG Yonghai;WEI Jinjia(School of Chemical Engineering and Technology,Xi'an Jiaotong University;State Key Laboratory of Multiphase Flow Engineering)
出处
《油气储运》
CAS
北大核心
2024年第1期49-56,共8页
Oil & Gas Storage and Transportation
基金
国家重点研发计划“氢能技术”重点专项项目“中低压纯氢与掺氢燃气管道系统事故特征演化及完整性管理”,2021YFB4001603。
关键词
天然气管道
泄漏检测
高斯烟羽模型
贝叶斯推理
MCMC算法
初始点
natural gas pipeline
leakage detection
Gaussian plume model
Bayesian inference
MCMC algorithm
initial point
