High power fuel cell simulator based on artificial neural network (Articolo in rivista)

Type
Label
  • High power fuel cell simulator based on artificial neural network (Articolo in rivista) (literal)
Anno
  • 2010-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1016/j.ijhydene.2009.09.071 (literal)
Alternative label
  • Chàvez-Ramirez, A. U. ; Muñoz-Guerrero, R. ; Duròn-Torres, S.M. ; Ferraro, M. ; Brunaccini, G. ; Sergi, F. ; Antonucci, V. ; Arriaga, L.G. (2010)
    High power fuel cell simulator based on artificial neural network
    in International journal of hydrogen energy; Pergamon-Elsevier Science Ltd., Oxford (Regno Unito)
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Chàvez-Ramirez, A. U. ; Muñoz-Guerrero, R. ; Duròn-Torres, S.M. ; Ferraro, M. ; Brunaccini, G. ; Sergi, F. ; Antonucci, V. ; Arriaga, L.G. (literal)
Pagina inizio
  • 12125 (literal)
Pagina fine
  • 12133 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 35 (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo
  • 21 (literal)
Note
  • Scopu (literal)
  • ISI Web of Science (WOS) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Chàvez-Ramirez A.U. - Departamento de Ingenier?´a Ele´ctrica, CINVESTAV-IPN. Av. Instituto Polite´cnico Nacional No. 2508, D.F. CP 07360, Mexico ; Muñoz-Guerrero R. - Departamento de Ingenier?´a Ele´ctrica, CINVESTAV-IPN. Av. Instituto Polite´cnico Nacional No. 2508, D.F. CP 07360, Mexico ; Duròn-Torres S.M. - Unidad Acade´mica de Ciencias Qu?´micas, Universidad Auto´noma de Zacatecas, Campus Siglo XXI, Edif. 6, Mexico ; Arriaga L.G. - Centro de Investigacio´n y Desarrollo Tecnolo´gico en Electroqu?´mica S.C., Parque Tecnolo´gico Quere´taro, Sanfandila, Pedro Escobedo, Quere´taro, Mexico (literal)
Titolo
  • High power fuel cell simulator based on artificial neural network (literal)
Abstract
  • Artificial Neural Network (ANN) has become a powerful modeling tool for predicting the performance of complex systems with no well-known variable relationships due to the inherent properties. A commercial Polymeric Electrolyte Membrane fuel cell (PEMFC) stack (5 kW) was modeled successfully using this tool, increasing the number of test into the 7 inputs - 2 outputs-dimensional spaces in the shortest time, acquiring only a small amount of experimental data. Some parameters could not be measured easily on the real system in experimental tests; however, by receiving the data from PEMFC, the ANN could be trained to learn the internal relationships that govern this system, and predict its behavior without any physical equations. Confident accuracy was achieved in this work making possible to import this tool to complex systems and applications. (literal)
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