380520 A Methodology for Leak Detection in Natural Gas Pipelines Under Different Consumer Usage Patterns

Wednesday, November 19, 2014: 3:59 PM
404 - 405 (Hilton Atlanta)
Xinghua Pan, Chemical Engineering, Texas A&M University, College Station, TX and M. Nazmul Karim, Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX

Model based fault detection is one of the most widely used approaches for industrial process fault diagnosis [1]. For leak detection in a natural gas pipeline, software methods such as model-based methods or statistics based methods have been applied [2-5]. The model-based leak detection strategy is based on the pressure and flow rate measurement at the inlet and outlet of the pipeline and an estimation algorithm from the natural gas flow model. Estimation algorithms proposed for a single natural gas pipeline leak detection include Unscented Kalman Filter, particle filter, and Extended Kalman Filter [6-9]. However, leak detection in natural gas pipeline networks involving different consumer usages has not been studied.

In this paper, we consider the effects of different consumer usages in a natural gas pipeline network. Two different consumer usages are considered which are constant industrial usage and household consumer. The household consumer usage varies based on temperature and the time of the day and can’t be predicted precisely. The effect of leak on the flow rate of the pipeline network is simulated using the non-isothermal modelling. An estimation algorithm is developed for natural gas pipeline fault detection (leak size and location) in the presence of different consumer usages. Furthermore, due to the length of the pipeline, there is a time delay of the effect of consumer usage at both ends of the pipeline. Based on the non-linear isothermal model of the natural gas flow in a pipeline and boundary condition of constant pressure at both ends, a simple model was derived showing the effect of consumer usage on the inlet and outlet flow rate at steady state.  With a random leak treated as an unknown input, an unknown input observer with time delay is designed based on the above model for leak detection. Two residuals are generated for the leak size and location identification. An alarm of leak is triggered according to the magnitude of the residuals.

[1] Isermann R. Model-based fault-detection and diagnosis–status and applications. Annual Reviews in control. 2005;29:71-85.

[2] Geiger IG. Principles of leak detection. 2005.

[3] Billmann L, Isermann R. Leak detection methods for pipelines. Automatica. 1987;23:381-5.

[4] Wang S, Carroll J. Leak Detection for Gas and Liquid Pipelines by Transient Modeling.  International Oil & Gas Conference and Exhibition in China 2006.

[5] Benkherouf A, Allidina A. Leak detection and location in gas pipelines.  Control Theory and Applications, IEE Proceedings D: IET; 1988. p. 142-8.

[6] Liu M, Zang S, Zhou D. Fast leak detection and location of gas pipelines based on an adaptive particle filter. International Journal of Applied Mathematics and Computer Science. 2005;15:541.

[7] Reddy HP, Narasimhan S, Bhallamudi SM, Bairagi S. Leak detection in gas pipeline networks using an efficient state estimator. Part-I: Theory and simulations. Computers & Chemical Engineering. 2011;35:651-61.

[8] Wan J, Yu Y, Wu Y, Feng R, Yu N. Hierarchical leak detection and localization method in natural gas pipeline monitoring sensor networks. Sensors. 2011;12:189-214.

[9] Tang W. Modeling, Estimation, and Control of Nonlinear Time-Variant Complex Processes: Texas Tech University; 2013.

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