长输液体管道泄漏监测方法研究
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摘要
长输管道作为一种重要的运输手段,早已在我国和世界工业中广泛应用。但由于种种原因,管道泄漏事故时有发生。为了减小管道泄漏造成的经济损失,以及对环境造成的严重污染,有必要对管道泄漏监测技术加以研究。已有的监测方法,或价格昂贵,或维修困难,或影响正常运行,针对长输管线来讲,都是不理想的。本文研究的重点是根据管道进出口处流量和压强的特征识别管道泄漏并确定泄漏点的位置。
为了从理论上指导泄漏监测方法的建立,本文用特征线法计算了管道泄漏时的控制方程。通过对输水管道和输油管道的泄漏数值计算发现,管道泄漏时管道上下游水力参数的综合变化规律不同于阀门启闭引起的水力参数的变化规律,这一规律的发现为泄漏识别方法的提出提供了依据。
依据管道泄漏时上下游水力参数具有独特变化规律的事实,本文提出了两种泄漏识别方法。一种方法是基于新息理论变点检测的管道泄漏识别方法。文中将基于多元假设的序列似然比检测方法与新息理论结合推导出基于新息理论的变点检测方法,利用BP神经网络非线性时间序列预测方法建立了管道进出口处水力参数的新息模型,然后利用本文提出的变点检测方法监测经新息模型产生的新息序列实现管道泄漏在线识别。另一种方法是基于半模糊聚类的长输管道泄漏识别方法。文中的半模糊聚类算法是通过将陈守煜的模糊聚类算法中的隶属度阈值化以后得到的,并利用该半模糊聚类算法将管道进出口处的水力参数看作指标特征值进行聚类,根据聚类结果判别管道是否发生泄漏。通过对实验输水管道以及实际输油管道的泄漏识别得到这两种方法的漏报警率为3.4%左右。
本文给出了两种泄漏点定位方法。一种是基于BP神经网络预测原理的压力梯度法。该方法采用BP神经网络预测摩阻系数,利用压力梯度法求得泄漏点的位置。另一种是由逆瞬态法和压力梯度法迭代求解泄漏点位置的方法。该方法将泄漏点前后的摩阻系数以及泄漏流量系数看作优化控制变量、水击方程的数值计算结果与测量值之差的绝对值作为控制目标,利用遗传算法优选泄漏点前后的摩阻系数,与压力梯度法结合不断迭代求解泄漏点的位置。通过输水管道的泄漏实验得到这两种方法漏点定位的平均误差分别为12%和4%左右。
本文编制了泄漏监测系统软件。软件包括界面程序、泄漏识别和泄漏点定位程序以及数据库。并将该软件安装到平湖油田的海底输油管线上进行了现场测试。
为了验证本文所做的具体工作,设计了长48.775m、管径53mm的输水管道,实验在大连理工大学的水力学实验室里进行。通过实验验证了管道泄漏以及阀门启闭工况下管道上下游流量和压强的变化规律。并利用管道泄漏监测系统软件分别识别了阀门启闭工况以及泄漏工况。
Long-distance pipeline, one of the most important transportation means, has been widely utilized around the world. However, leak in pipeline often occurs owing to different reasons. To reduce the economic losses and environmental destruction, it is necessary to study the leak detection methods. Traditional methods are not suitable for the long-distance pipeline due to their expensive price, difficult maintenance and influence on normal running, etc. That is the motivation of this thesis. The research in this thesis emphasizes on the leak identification and the leak point location according to the characteristics of the flow and the pressure at inlet and outlet of the pipeline.
In this thesis, the method of characteristics is used to calculate the governing equations including the leak. It is noticed that the variation of the hydraulic parameters under the leak situation is different from those caused by opening and closing the valve at inlet and outlet. This finding provides the foundation to develop the leak identification methods in theory.
According to the change law of hydraulic parameters at inlet and outlet of the pipeline, two methods for leak identification have been presented. One is a new abrupt change method, which is based on innovation theory. This new method is deduced by combining the extended sequential probability test and the innovation theory. The innovation model of the hydraulic parameters is established by prediction method of nonlinear time series based on BP neural network. Thus, the on-line leak identification can be fulfilled by monitoring the innovation series given by the innovation model. The other is the semi-fuzzy clustering method. This method is obtained through limiting the membership degree of Chen's fuzzy clustering method. This method is used to classify the hydraulic parameters at inlet and outlet of the pipeline and to detect the leak in pipeline. The probability of missing alarm of these two methods is 3.4% through the leak identification of experiments and actual tests.
Two methods for locating leak point have been developeded. One is a pressure grade method based on forcasting theory of BP neural network. The other is iterative method based on the inverse transient method and pressure gradient method. In this technique, decision variables are friction factors and objective function minimizes the difference of water heads between caculated valves by waterhammer equations and measured valves at measured sites. The mean location error of two methods is respectively 12% and 4% based on experimental tests.
Software for leak detection system, composed of interface program, leak monitoring program and leak point location program, was developed. The software has been installed to the oil pipeline in Pinghu oil field.
To verify the developed methods, a water pipe with a length of 48.775m and a diameter of 53mm has been set up in the hydraulics laboratory of Dalian University of Technology. The change law of the hydraulic parameters at inlet and outlet has been validated under the situation of leak and valve opening and closing. The software for leak detection system is also used to identify the situation of leak and valve opening and closing respectively in the experimental pipeline.
为了从理论上指导泄漏监测方法的建立,本文用特征线法计算了管道泄漏时的控制方程。通过对输水管道和输油管道的泄漏数值计算发现,管道泄漏时管道上下游水力参数的综合变化规律不同于阀门启闭引起的水力参数的变化规律,这一规律的发现为泄漏识别方法的提出提供了依据。
依据管道泄漏时上下游水力参数具有独特变化规律的事实,本文提出了两种泄漏识别方法。一种方法是基于新息理论变点检测的管道泄漏识别方法。文中将基于多元假设的序列似然比检测方法与新息理论结合推导出基于新息理论的变点检测方法,利用BP神经网络非线性时间序列预测方法建立了管道进出口处水力参数的新息模型,然后利用本文提出的变点检测方法监测经新息模型产生的新息序列实现管道泄漏在线识别。另一种方法是基于半模糊聚类的长输管道泄漏识别方法。文中的半模糊聚类算法是通过将陈守煜的模糊聚类算法中的隶属度阈值化以后得到的,并利用该半模糊聚类算法将管道进出口处的水力参数看作指标特征值进行聚类,根据聚类结果判别管道是否发生泄漏。通过对实验输水管道以及实际输油管道的泄漏识别得到这两种方法的漏报警率为3.4%左右。
本文给出了两种泄漏点定位方法。一种是基于BP神经网络预测原理的压力梯度法。该方法采用BP神经网络预测摩阻系数,利用压力梯度法求得泄漏点的位置。另一种是由逆瞬态法和压力梯度法迭代求解泄漏点位置的方法。该方法将泄漏点前后的摩阻系数以及泄漏流量系数看作优化控制变量、水击方程的数值计算结果与测量值之差的绝对值作为控制目标,利用遗传算法优选泄漏点前后的摩阻系数,与压力梯度法结合不断迭代求解泄漏点的位置。通过输水管道的泄漏实验得到这两种方法漏点定位的平均误差分别为12%和4%左右。
本文编制了泄漏监测系统软件。软件包括界面程序、泄漏识别和泄漏点定位程序以及数据库。并将该软件安装到平湖油田的海底输油管线上进行了现场测试。
为了验证本文所做的具体工作,设计了长48.775m、管径53mm的输水管道,实验在大连理工大学的水力学实验室里进行。通过实验验证了管道泄漏以及阀门启闭工况下管道上下游流量和压强的变化规律。并利用管道泄漏监测系统软件分别识别了阀门启闭工况以及泄漏工况。
Long-distance pipeline, one of the most important transportation means, has been widely utilized around the world. However, leak in pipeline often occurs owing to different reasons. To reduce the economic losses and environmental destruction, it is necessary to study the leak detection methods. Traditional methods are not suitable for the long-distance pipeline due to their expensive price, difficult maintenance and influence on normal running, etc. That is the motivation of this thesis. The research in this thesis emphasizes on the leak identification and the leak point location according to the characteristics of the flow and the pressure at inlet and outlet of the pipeline.
In this thesis, the method of characteristics is used to calculate the governing equations including the leak. It is noticed that the variation of the hydraulic parameters under the leak situation is different from those caused by opening and closing the valve at inlet and outlet. This finding provides the foundation to develop the leak identification methods in theory.
According to the change law of hydraulic parameters at inlet and outlet of the pipeline, two methods for leak identification have been presented. One is a new abrupt change method, which is based on innovation theory. This new method is deduced by combining the extended sequential probability test and the innovation theory. The innovation model of the hydraulic parameters is established by prediction method of nonlinear time series based on BP neural network. Thus, the on-line leak identification can be fulfilled by monitoring the innovation series given by the innovation model. The other is the semi-fuzzy clustering method. This method is obtained through limiting the membership degree of Chen's fuzzy clustering method. This method is used to classify the hydraulic parameters at inlet and outlet of the pipeline and to detect the leak in pipeline. The probability of missing alarm of these two methods is 3.4% through the leak identification of experiments and actual tests.
Two methods for locating leak point have been developeded. One is a pressure grade method based on forcasting theory of BP neural network. The other is iterative method based on the inverse transient method and pressure gradient method. In this technique, decision variables are friction factors and objective function minimizes the difference of water heads between caculated valves by waterhammer equations and measured valves at measured sites. The mean location error of two methods is respectively 12% and 4% based on experimental tests.
Software for leak detection system, composed of interface program, leak monitoring program and leak point location program, was developed. The software has been installed to the oil pipeline in Pinghu oil field.
To verify the developed methods, a water pipe with a length of 48.775m and a diameter of 53mm has been set up in the hydraulics laboratory of Dalian University of Technology. The change law of the hydraulic parameters at inlet and outlet has been validated under the situation of leak and valve opening and closing. The software for leak detection system is also used to identify the situation of leak and valve opening and closing respectively in the experimental pipeline.
引文
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