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Please use this identifier to cite or link to this item: http://acervodigital.unesp.br/handle/11449/34808
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dc.contributor.authorSaggioro, N. J.-
dc.contributor.authorCagnon, J. A.-
dc.contributor.authorda Silva, I. N.-
dc.contributor.authorIEEE-
dc.date.accessioned2014-05-20T15:24:09Z-
dc.date.accessioned2016-10-25T17:58:18Z-
dc.date.available2014-05-20T15:24:09Z-
dc.date.available2016-10-25T17:58:18Z-
dc.date.issued2002-01-01-
dc.identifierhttp://dx.doi.org/10.1109/IJCNN.2002.1007675-
dc.identifier.citationProceeding of the 2002 International Joint Conference on Neural Networks, Vols 1-3. New York: IEEE, p. 1258-1262, 2002.-
dc.identifier.issn1098-7576-
dc.identifier.urihttp://hdl.handle.net/11449/34808-
dc.identifier.urihttp://acervodigital.unesp.br/handle/11449/34808-
dc.description.abstractIn most of the cases, the systems of water distribution from groundwater wells use electrical submersible pumps. All electrical energy is applied to the pumps; however, other components (pipes, valves, etc.) of these systems are also responsible by the higher or lower consumption of electric energy. The supervisors and operators of the systems should thus have knowledge of the global energetic behavior of the process in order to administrate it properly. This work suggests a 'Global Energetic Efficiency Indicator' for groundwater wells by using mathematical equations and neural networks. Simulation results will be presented in order to demonstrate the validity of the proposed approach.en
dc.format.extent1258-1262-
dc.language.isoeng-
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)-
dc.sourceWeb of Science-
dc.titleA neural approach for determination of global energetic efficiency indicator in groundwater wellsen
dc.typeoutro-
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)-
dc.description.affiliationState Univ São Paulo, FE, DEE, UNESP, BR-17033360 Bauru, SP, Brazil-
dc.description.affiliationUnespState Univ São Paulo, FE, DEE, UNESP, BR-17033360 Bauru, SP, Brazil-
dc.identifier.doi10.1109/IJCNN.2002.1007675-
dc.identifier.wosWOS:000177402800224-
dc.rights.accessRightsAcesso restrito-
dc.relation.ispartofProceeding of the 2002 International Joint Conference on Neural Networks, Vols 1-3-
Appears in Collections:Artigos, TCCs, Teses e Dissertações da Unesp

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