基于无线传感器网络的气体泄漏源快速定位方法研究

为及时发现大气环境中危险化学品泄漏事故,快速准确判断泄漏源位置,实现有效的监测监控,开发出集气体质量浓度信息采集、时间校准、无线收发等功能于一体的集成探测模块。在此基础上,建立基于无线传感器网络(WSN)的气体泄漏实时监测平台,提出实时监测数据和高斯扩散模型相结合的气体泄漏源快速定位方法。通过开展实际场地泄漏试验,实现气体泄漏源的快速定位,并分析试验系统的敏感度。结果表明:气体泄漏扩散受风力影响很大;合理布局探测器,能有效提高泄漏源定位精度,为危险气体泄漏事故的应急决策提供决策参考。     

压力容器气体非稳态泄漏模型研究

为计算气体在非稳态泄漏过程中的泄漏率,提高危害后果评估的量化水平,对压力容器失效后气体泄漏过程进行了研究。基于现有的初始泄漏率模型,结合实际泄漏过程中压力容器内各项状态参数的动态变化规律,构建气体非稳态泄漏模型,并通过计算实例进行分析和验证。结果表明,该模型可计算压力容器气体非稳态泄漏过程中(包括音速泄漏阶段和亚音速泄漏阶段)任意时刻容器内的各项状态参数值和孔口处气体的平均泄漏率;同时,对于储存压力较高(大于3.0 MPa)的容器,提出近似计算总平均泄漏率的2种简化方法。     

基于改进遗传-单纯形混合算法的危险气体泄漏溯源分析

当发生危险气体泄漏时,确定其泄漏位置和泄漏源强,是制定应急方案的基础和依据之一。当无法直接确定泄漏位置、测量泄漏源强时,就需要通过有限的几个监测点,反演出可能的泄漏位置和泄漏源强,现有方法存在收敛过慢、初值敏感、参数过多等问题。描述一种结合改进遗传算法和单纯形法的IGA-NM混合算法,可用于快速反算气体泄漏的位置和源强。IGA-NM混合算法既避免了GA的收敛过慢,又避免了NM初值敏感,兼顾了全局优化。与GA、NM相比,IGA-NM混合算法的计算速度更快,计算误差更小。最后,应用IGA-NM混合算法,基于WebGIS设计了一套计算气体泄漏源强和位置的计算机程序,简化了输入参数,使用方便,可适用于气体泄漏应急监测、大气污染源溯源反查等场合     

沸腾液体膨胀蒸气爆炸滞止时间模型

为研究液化气体泄漏时发生沸腾液体膨胀蒸气爆炸(BLEVE)现象,基于杨-拉普拉斯公式及汽泡形成和成长的条件特性,应建立液化气气体泄漏发生BLEVE现象的滞止时间模型。根据泄漏气体的喉部泄漏特性,建立气体泄漏速度公式;依据泄漏压力容器内部气体比体积变化情况,建立压力容器内部比体积数学模型;基于气体泄漏速度公式及压力容器内部比体积数学模型得到液化气体泄漏发生BLEVE现象的滞止时间数学模型。结果表明,过热度足够高时,将使汽泡内部的饱和蒸气压强打破汽泡表面张力形成的势垒,汽泡破裂,发生蒸气爆炸现象,在汽泡形成和上升过程中,汽泡平均上升时间较长,实验结果验证了模型的有效性。     

海上气井环空带压地面监测诊断方法研究

为有效管理海上气井环空带压(SCP),建立海上气井安全屏障模型,分析得出引起SCP的23种气体泄漏途径;根据SCP特性及其形成原因,建立环空压力监测和组分分析结合的SCP诊断方法;针对油管失效泄漏这一最危险因素,建立利用环空保护液位置判断泄漏位置的方法,以及通过监测井口泄压速率诊断泄漏程度的方法。综合压力、流量、温度、液面及气体组分等监测指标,建立适合海上生产平台使用的SCP地面在线监测系统。结果表明:对于有SCP问题的天然气井,压力监测与组分分析结合能够排除热膨胀引起的环空压力;为准确判断引起SCP的泄漏量和泄漏位置,需要测量流量、温度和环空液位高度等参数。     

化工罐区重质气体多源泄漏扩散实验研究

在模拟实验平台开展了罐区重质气体多源泄漏扩散的实验研究,考察多泄漏源同时泄漏时,泄漏源在罐区的位置、泄漏源间距对罐区重质气体漏扩散过程的影响。结果表明:泄漏源越靠近罐区边缘,重质气体扩散范围越大;泄漏源越靠近罐区中心区域,周围罐的阻碍作用较大,中心区域的重质气体浓度越高;泄漏源间的间距越小,泄漏源中间区域的重气浓度越大,泄漏源间的间距增大,气体扩散范围也增大,事故影响范围越大;泄漏压力、体积速率总和相同时,在一定的距离范围内,多源同时泄漏时空间各点的重质气体浓度与各泄漏源单独泄漏时空间各点重质气体浓度总和基本一致。     

室内有害气体连续泄漏扩散质量浓度模型的研究与分析

以C02为对象,对室内空间气体连续泄漏扩散过程进行试验研究,并对室内CO2气体泄漏扩散的均一质量浓度模型、两厢质量浓度模型和室内半球质量浓度模型进行研究.将理论模型计算值与不同位置测量点的试验数据进行比较分析.3种质量农度模型均表示区域质量浓度的变化,理论模型计算值与试验数据均有些偏差;远离泄漏源处,偏差较小.室内空间不同位置3个模型预测值相对大小会发生变化.对于泄漏源附近及低于泄漏源处,3种质量浓度模型预测结果误差较大;对于高于泄漏源的位置,模型预测结果较好,然而质量浓度均出现振荡不稳定的现象.由于重力沉降作用,下部空间气体质量浓度较大,上部空间气体质量浓度较小.泄漏刚开始阶段,远离泄漏源处,试验测试值与理论模型值相比有一个廷滞期,理论预测值偏差较大.     

LNG储罐泄漏危险性计算及影响因素分析

针对LNG储罐泄漏气体扩散模拟分析过程中存在计算和分析过程复杂的问题,选取适当的气体扩散模型,对危险气体的扩散进行模拟和分析,绘制蒸汽扩散UFL(爆炸上限)、LFL(爆炸下限)、1/2LFL浓度等值线图,实现蒸汽扩散伤害分区的准确划分,提高了计算速率和精确度。并利用程序模拟分析了风速、地表粗糙度、泄漏速率等因素对LNG泄漏气体扩散影响。研究结果表明,当风速方向和泄漏源泄漏方向相同时,蒸汽扩散距离和危害范围随风速增大呈减小趋势;蒸汽在下风向扩散距离随着地表粗糙度的增大而减小;扩散距离和危害范围随泄漏速率的增大而增大。     

基于静电传感信号的小孔泄漏探测方法与试验

根据高压气体从小孔喷出时在喷出区域附近积聚大量电荷的现象,提出基于静电传感信号的小孔泄漏探测原理和信号处理方法,并开展探索性静电感知试验。首先,分析气体从小孔泄漏喷出导致附近区域异常带电的机制,并引入基于静电传感的泄漏探测理论方法;然后,针对静电信号内含信息提取问题,提出一种基于稀疏分解的信号降噪与特征提取方法,进一步在压力管道气体泄漏试验平台开展气体泄漏喷出静电感知试验;最后,对小孔泄漏静电探测应用场景展开讨论。结果表明：泄漏发生时喷射气体的静电信号特征十分活跃,且信号平均峰值与喷射压力呈二次正相关,静电传感技术有望为泄漏监测提供一种新的手段。     

基于差分进化-NM单纯形法的危化品泄漏源定位*

为确定危化品泄漏源的强度和位置,提出1种基于DE-NM算法的危化品泄漏源定位方法,以监测浓度与扩散模型计算浓度的误差作为优化目标,在差分进化过程中每隔一定代数执行单纯形法,使得二者误差最小的源强和位置即为最优定位结果。研究结果表明:DE-NM算法能够快速有效地反算出泄漏源的强度和位置,满足应急响应的要求;同时,能够避免DE算法的过早收敛,以及NM算法对初值敏感的问题,有效降低单一算法对定位结果精度造成的不利影响。     

基于模糊相似优先比的供水管网渗漏同步诊断*

为加强城市供水管网渗漏诊断能力,采用基于模糊相似优先比的漏损判别方法实现供水管网漏损定位及漏损程度的同步诊断。通过MATLAB软件调用最新版EPANET V2.2建立供水管网模型,在管段中间加入扩散器模拟单次渗漏事件,通过更改扩散器系数实现渗漏量的控制;基于压力驱动水力分析得到各节点压力变化,遍历模拟各管段漏损后,通过建立节点压力灵敏度矩阵,采用K均值聚类法进行监测点布置;在此基础上,在易渗漏管段模拟产生不同渗漏级别的渗漏事件,以监测点压力变化值构建源范例库,在熵权法的基础上,采用模糊相似优先比方法同步诊断渗漏位置及渗漏程度。以某一实际管网为例,模拟产生50例历史渗漏事件,采用模糊相似优先比同步诊断新渗漏事件的渗漏位置及渗漏程度,并对比3种权重方法。结果表明:模糊相似优先比法可有效地实现渗漏定位与渗漏程度的同步诊断。     

基于广义概念的城市燃气管道泄漏精确定位

为了准确地检测城市燃气管道泄漏,提出了一种基于广义概念的管道泄漏检测定位方法。声发射技术对于管道泄漏的检测、定位是一个极好的工具,但由于泄漏源的传播容易受到周围背景噪声以及复杂工况的影响,其定位误差较大。基于时延估计的互相关信号处理方法被广泛用于管道泄漏检测定位,但由于泄漏应力波传播通道的动态特性,使得源信号在传播过程中会产生波形变化,给互相关函数峰值位置的确定带来困难。由此引入广义相关分析方法,通过对信号进行前置滤波,在一定程度上减少了传播通道动态特性因素对泄漏点定位的不利影响,得到了更为准确的时延估值。在此基础上,通过模拟实验,编写Matlab神经网络代码,构造GRNN模型,进一步预测定位。结果表明,GRNN预测的声发射检测值、互相关定位值以及广义相关定位值,相比之前定位精度分别得到提高,其中基于广义相关的延时估计方法定位最为精确,将该方法用于工程实际中,可以更加精确地定位出泄漏点。     

Integrated-signal-based leak location method for liquid pipelines

Leaks in pipelines can cause major incidents resulting in both human injuries and financial losses. Among the considerable leak detection and location methods, the Negative Pressure Wave (NPW) based method has been widely used in locating leaks in liquid pipelines. The NPW based method only monitors the pressure changes at two ends of a pipeline. But the pressure is apt to be fixed by the end equipment and the change of it induced by a small or slow leakage is too small to be detected, which limit the application of the NPW based method in these situations. This paper presents a novel leak location method based on integrated signal, which is a combination of the pressure and flow rate signals. The representation of the integrated signal is derived from the transient analysis of the leakage. For the change of the integrated signal induced by a leakage is larger than the pressure change and it is also unaffected by the end equipment, the proposed method can be used to detect and locate small or slow leakage in a pipeline and can also be used in pipelines which end pressures are fixed by some kinds of equipment. The validation of the proposed method also confirms its advantages.     

Leak location of pipelines based on characteristic entropy

The leakage of oil/gas pipelines is one of the major factors to influence the safe operation of pipelines. So it is significant to detect and locate the exact pipeline leakage. A novel leak location method based on characteristic entropy is proposed to extract the input feature vectors. In this approach, the combination of wavelet packet and information entropy is called “wavelet packet characteristic entropy” (WP-CE). The combination of empirical mode decomposition and information entropy is called “empirical mode decomposition characteristic entropy” (EMD-CE). Both pressure signal and flow signal of low noise and high noise of pipeline leakage are decomposed to extract the characteristic entropy. The location of pipeline leak is determined by the combination of the characteristic entropy as the input vector and particle swarm optimization and support vector machine method (PSO-SVM). The results of proposed leak location method are compared with those of PSO-SVM based on physical parameters. Under the condition of high noise, the results of proposed leak location method are better than those of PSO-SVM based on physical parameters.     

Image rotation method for identification of NPW signals in the localization of pipeline leakage

Gas and oil are mainly transported through long-distance pipelines on land. Pipeline leaks lead to severe hazards to the environment and economy and even imperil human lives. Negative pressure wave (NPW)-based methods are fast and effective for leak monitoring and localization. The key problem for an NPW-based method is to determine the NPW and its arrival time, which is characterized by the knee point in the time domain signal. In this paper, an image rotation method is proposed based on the shape characteristic of the time domain signal induced by an NPW. Through image rotation, the knee point turns into the highest point, which is easy to detect. To verify the performance of the proposed method, leakage experiments were conducted on liquid and gas pipeline models. Previously developed FBG pipe fixture sensors were used to detect an NPW. These sensors were equidistantly installed on the pipeline, forming a sensor array. Based on the sensing array, a novel leak localization algorithm was used to compute the leakage position. The experimental results indicated that the image rotation method has good performance for identifying an NPW, even though many noise- and pressure-induced fluctuations exist in the signals. This method enables automated real-time monitoring and has potential for practical application.     

Real-time leak detection in oil pipelines using an Inverse Transient Analysis model

Based on Inverse Transient Analysis (ITA) method, a real-time leak detection method is proposed to capture leak location and the associated leak rate in oil pipe conveyance systems. In the proposed approach, location and flow rate of leak (if any), the fluid properties, as well as physical parameters of the system, are calculated in consecutive periods through minimizing the discrepancy between the calculated and measured flow parameters of the system. The method of characteristics is employed to numerically calculate the transient responses of the system and the genetic algorithm is utilized as the optimization engine. The proposed approach was applied to several real pipeline systems in which the required transient flow data are either directly collected from the field or fabricated with a third-party numerical software. Extensive numerical explorations were conducted to investigate the performance of the proposed method in real-time leak detection and to determine the extent to which field data errors, stemming from Supervisory Control and Data Acquisition (SCADA) systems and measurement equipment, affect the leak flow rate and location detectability of the proposed approach. The results show that the proposed approach provides promising results under a variety of transient and steady-state flow conditions even in the case with small leak flow rate of around 2% of the line rate. The results also reveal that the noises in the measurement data and the errors originated from SCADA systems do not significantly compromise the leak detectability of the proposed approach, confirming that the proposed approach can be utilized in practice.     

Locating the gas leakage source in the atmosphere using the dispersion wave method

A new and simple method for locating emission source was proposed in this work based on gas dynamic dispersion information. The simulation of the unsteady state dispersion of leakage gas emission from the geosequestration project showed that the transportation process of emission gases in the atmosphere is similar to wave propagation, and the time parameter of the dispersion wave is linearly related to the downwind distance. Therefore, monitoring the dispersion wave at different downwind positions can be used to estimate the leakage source position. An estimation formula for locating emission sources was derived. First, an estimation formula for locating emission sources was derived under some initial assumptions. Then, the deviation of the location formula was investigated using a computational fluid dynamics (CFD) model and analytic solution to get the offset distance under different conditions. The results showed that the average distance is stable for a certain atmosphere and terrestrial conditions. This method needs no more than 3 sensors’ dynamic information to locate the emission source, and hence it is highly useful for conditions with limited sensors. A numerical test demonstrated that the absolute error of the source estimation is within the range of 1–30 m. Finally, experimental tests were conducted to verify the feasibility of the source location with dispersion waves. Therefore, the dispersion wave monitor is a potentially simple and feasible way to estimate the source location for gas emission event management with limited sensors in the process industries.