Title
Inverse neural networks based optimization of a PEMFC performances - Study of the PT/C content effect
Date Issued
01 January 2011
Access level
metadata only access
Resource Type
conference paper
Author(s)
Piuleac C.
Curteanu S.
Cañizares P.
Rodrigo M.
Pinar F.
Úbeda D.
Lobato J.
University of Castilla-LaMancha
Publisher(s)
ENEA
Abstract
Modeling and optimization procedures based on direct and inverse neural networks (NN5) methodology was designed on a Polybenzimidazole (PBI)-based polymer electrolyte membrane fuel cell (PEMFC) to obtain a high performance (maximum power). Different Pt/C percentages (20-60%) at anode and cathode, in different combinations, and different operation temperatures (125- 200°C) from a laboratory experiments scale were considered. The optimum operating fuel cell conditions which lead to high efficiency were identified using a neural network based procedure.
Start page
37
End page
38
Language
English
OCDE Knowledge area
Ciencias de la computación
Subjects
Scopus EID
2-s2.0-84923596890
Resource of which it is part
EFC 2011 - Proceedings of the 4th European Fuel Cell Piero Lunghi Conference and Exhibition
ISBN of the container
978-888286254-1
Conference
4th European Fuel Cell Piero Lunghi Conference and Exhibition, EFC 2011
Sources of information:
Directorio de Producción Científica
Scopus