Detection and on-line prediction of leak magnitude in a gas pipeline using an acoustic method and neural network data processing

dc.creatorSantos, R. B.
dc.creatorSousa, E. O. de
dc.creatorSilva, F. V. da
dc.creatorCruz, S. L. da
dc.creatorFileti, A. M. F.
dc.date2014-03-01
dc.date2014-07-17T17:41:38Z
dc.date2015-11-26T11:47:57Z
dc.date2014-07-17T17:41:38Z
dc.date2015-11-26T11:47:57Z
dc.date.accessioned2018-03-28T20:51:33Z
dc.date.available2018-03-28T20:51:33Z
dc.identifierBrazilian Journal of Chemical Engineering. Brazilian Society of Chemical Engineering, v. 31, n. 1, p. 145-153, 2014.
dc.identifier0104-6632
dc.identifierS0104-66322014000100014
dc.identifier10.1590/S0104-66322014000100014
dc.identifierhttp://dx.doi.org/10.1590/S0104-66322014000100014
dc.identifierhttp://www.scielo.br/scielo.php?script=sci_arttext&pid=S0104-66322014000100014
dc.identifierhttp://www.repositorio.unicamp.br/jspui/handle/REPOSIP/26148
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/26148
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1236477
dc.descriptionConsidering the importance of monitoring pipeline systems, this work presents the development of a technique to detect gas leakage in pipelines, based on an acoustic method, and on-line prediction of leak magnitude using artificial neural networks. On-line audible noises generated by leakage were obtained with a microphone installed in a 60 m long pipeline. The sound noises were decomposed into sounds of different frequencies: 1 kHz, 5 kHz and 9 kHz. The dynamics of these noises in time were used as input to the neural model in order to determine the occurrence and the leak magnitude. The results indicated the great potential of the technique and of the developed neural network models. For all on-line tests, the models showed 100% accuracy in leak detection, except for a small orifice (1 mm) under 4 kgf/cm² of nominal pressure. Similarly, the neural network models could adequately predict the magnitude of the leakages.
dc.description145
dc.description153
dc.languageen
dc.publisherBrazilian Society of Chemical Engineering
dc.relationBrazilian Journal of Chemical Engineering
dc.rightsaberto
dc.sourceSciELO
dc.subjectPipeline network
dc.subjectLeak detection
dc.subjectNeural networks
dc.titleDetection and on-line prediction of leak magnitude in a gas pipeline using an acoustic method and neural network data processing
dc.typeArtículos de revistas


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