Publication
Leak diagnosis in pipelines using a combined artificial neural network approach
Journal Article (2021)
Journal
Control Engineering Practice
Pages
104677
Volume
107
Doc link
https://doi.org/10.1016/j.conengprac.2020.104677
File
Authors
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Perez Perez, Esvan de Jesus
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Lopez Estrada, Francisco Ronay
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Valencia, Guillermo
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Torres, Lizeth A
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Puig Cayuela, Vicenç
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Mina Antonio, Jesús Darío
Abstract
Leakages in pipelines affect the reliability of fluid transport systems causing environmental damages, economic losses, and pressure reduction at the delivery points. Therefore, this paper presents a methodology to detect and locate water leaks in pipelines by using artificial neural networks (ANN) techniques and online measurements of pressure and flow rate. Contrary to reported works in the literature, the proposed method estimates the friction factor of the pipe and uses this information as an input to compute the leak position. Data generated from a validated numerical simulator was used to enrich the data-training set for the ANN. Various leak scenarios were considered to characterize pressure losses and their differentials in different sections of the pipeline. Finally, the algorithm was tested experimentally in a pilot plant. The results demonstrate good performance and the applicability of the proposed method.
Categories
control theory.
Scientific reference
E.d. Perez Perez, F.R. Lopez Estrada, G. Valencia, L.A. Torres, V. Puig and J.D. Mina. Leak diagnosis in pipelines using a combined artificial neural network approach. Control Engineering Practice, 107: 104677, 2021.
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