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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 343Modeling Constructional Parameters of a Solid...

Modeling Constructional Parameters of a Solid Oxide Fuel Cell by Using an Artificial Neural Network

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Abstract:

An Artificial Neural Network (ANN) can predict an objects behavior with no algorithmic solution merely by utilizing available experimental data. The error backpropagation algorithm was used for an ANN training procedure. There are SOFC features mainly architectural in nature that cannot be expressed in numerical form or where numerical expression is difficult to obtain, i.e. electrolyte type, anode type, cathode type etc. In those situations a hybrid model (H-ANN) which contains the ANN model and mathematical expressions can be applied. The H-ANN is able to predict cell voltage with knowledge of minimum physical factors.

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Construction Materials and Computer Engineering II

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Periodical:

Applied Mechanics and Materials (Volume 343)

Pages:

69-75

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.343.69

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Online since:

July 2013

Authors:

Jarosław Milewski, Konrad Świrski

Keywords:

Artificial Neural Network (ANN), Constructional Parameters, Fuel Cell (FC), Solid Oxide

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© 2013 Trans Tech Publications Ltd. All Rights Reserved

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