Artificial Neural Network-Based Model for Calculating the Flow Composition Influence of Solid Oxide Fuel Cell

The paper presents use of an artificial neural network (ANN) for predicting the thermal-flow behavior of a solid oxide fuel cell with no algorithmic solution merely by utilizing available experimental data. The error backpropagation algorithm was used for an ANN training procedure.

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Bibliographische Detailangaben
Veröffentlicht in:	Journal of electrochemical energy conversion and storage 2014-04, Vol.11 (2), p.np-np
Hauptverfasser:	Milewski, Jarosław, Świrski, Konrad
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Applied sciences Artificial neural networks Back propagation Direct energy conversion and energy accumulation Electrical engineering. Electrical power engineering Electrical power engineering Electrochemical conversion: primary and secondary batteries, fuel cells Energy Energy. Thermal use of fuels Equipments for energy generation and conversion: thermal, electrical, mechanical energy, etc Exact sciences and technology Fuel cells Learning theory Mathematical models Neural networks Solid oxide fuel cells
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Beschreibung
Zusammenfassung:	The paper presents use of an artificial neural network (ANN) for predicting the thermal-flow behavior of a solid oxide fuel cell with no algorithmic solution merely by utilizing available experimental data. The error backpropagation algorithm was used for an ANN training procedure.
ISSN:	2381-6872 1550-624X 2381-6910 1551-6989
DOI:	10.1115/1.4025922