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

Konrad Świrski , Jarosław Milewski

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.
Author Konrad Świrski (FPAE / IHE)
Konrad Świrski,,
- The Institute of Heat Engineering
, Jarosław Milewski (FPAE / IHE)
Jarosław Milewski,,
- The Institute of Heat Engineering
Journal seriesApplied Mechanics and Materials, ISSN 1662-7482
Issue year2013
Vol2
Pages69-75
Keywords in Englishartificial neutral network, contructional parameters
DOIDOI:10.4028/www.scientific.net/AMM.343.69
URL http://www.scientific.net/AMM.343.69
Languageen angielski
Score (nominal)7
Score sourcejournalList
ScoreMinisterial score = 7.0, 26-11-2019, ArticleFromJournal
Ministerial score (2013-2016) = 7.0, 26-11-2019, ArticleFromJournal
Publication indicators Scopus Citations = 2
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