Role and characterization of leaks under transient conditions

The significance and impact of leaks in a pipeline system creates new opportunities of leak detection. In essence, the concept is to use the pressure response from a transient event to locate and size a leak. Previously, Brunone (1999), determined both the location and size of a leak on the basis of the pressure trace during a transient event at a measurement section on the basis of the well-known properties of pressure waves. More recently, formal inverse transient algorithms have been developed. The goal in this study is to see if the genetic inverse transient procedure can correctly locate and size a leak in a "blind test". More specifically, the pressure signal at the downstream end of the system as well as the basic pipe properties will be fed to the inverse procedure to see if the predicted existence, location and magnitude of the leak can be accurately determined. The paper reviews the results of the blind calibration procedure as well as summarizing the key background required to understand these developments. The significance of this study data to the later quality problem, and particularly to the danger of contamination of the pipe contents, are given special emphasis.

[1]  Li-Chung Chen,et al.  The Inverse Method as a Tool for Calibration and Leak Detection , 1995 .

[2]  Bruno Brunone,et al.  Transient Test-Based Technique for Leak Detection in Outfall Pipes , 1999 .

[3]  Manoj Kumar OPTIMIZATION USING GENETIC ALGORITHMS , 1998 .

[4]  David E. Goldberg,et al.  Genetic Algorithms in Pipeline Optimization , 1987 .

[5]  Dragan Savic,et al.  Genetic Algorithms for Least-Cost Design of Water Distribution Networks , 1997 .

[6]  Bryan W. Karney,et al.  EFFICIENT CALCULATION OF TRANSIENT FLOW IN SIMPLE PIPE NETWORKS , 1992 .

[7]  Hanif M. Chaudhry,et al.  Applied Hydraulic Transients , 1979 .

[8]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[9]  Dragan Savic,et al.  WATER NETWORK REHABILITATION WITH STRUCTURED MESSY GENETIC ALGORITHM , 1997 .

[10]  Angus R. Simpson,et al.  Optimisation of large-scale water distribution system design using genetic algorithms , 1998 .

[11]  A. Simpson,et al.  An Improved Genetic Algorithm for Pipe Network Optimization , 1996 .

[12]  Godfrey A. Walters,et al.  OPTIMAL LAYOUT OF TREE NETWORKS USING GENETIC ALGORITHMS , 1993 .

[13]  Angus R. Simpson,et al.  Genetic algorithms compared to other techniques for pipe optimization , 1994 .

[14]  G. V. Loganathan,et al.  Pipe Network Optimization , 1998 .

[15]  William H. Clingenpeel Optimizing pump operating costs , 1983 .

[16]  K Sridharan,et al.  Inverse transient analysis in pipe networks , 1996 .

[17]  E. Benjamin Wylie,et al.  Fluid Transients in Systems , 1993 .

[18]  Graeme C. Dandy,et al.  Genetic algorithms compared to other techniques for pipe optimization , 1994 .