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Research Papers

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

[+] Author and Article Information
Jarosław Milewski

Associate Professor
e-mail: milewski@itc.pw.edu.pl

Konrad Świrski

Associate Professor
e-mail: swirski@itc.pw.edu.pl
Institute of Heat Engineering,
Faculty of Power and Aeronautical Engineering,
Warsaw University of Technology,
Warsaw 00-665, Poland

1Corresponding author.

Contributed by the Advanced Energy Systems Division of ASME for publication in the JOURNAL OF FUEL CELL SCIENCE AND TECHNOLOGY. Manuscript received November 20, 2012; final manuscript received July 30, 2013; published online December 4, 2013. Editor: Nigel M. Sammes.

J. Fuel Cell Sci. Technol 11(2), 021001 (Dec 04, 2013) (5 pages) Paper No: FC-12-1117; doi: 10.1115/1.4025922 History: Received November 20, 2012; Revised July 30, 2013

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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Figures

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Fig. 1

Results of the training process for 7-8-1 network architecture

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Fig. 2

Results of simulation for the testing data, positive values of voltage are generated only for the hydrogen and carbon monoxide, there are no curves for CO2, N2, and He

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Fig. 3

Results of the training process for 3-4-1 network architecture

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Fig. 4

Results of the simulation for the testing data. Positive values of voltage are generated only for the oxygen, and there is no curve for N2.

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Fig. 5

Results of the training process for 9-3-1 network architecture

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Fig. 6

Results of simulation for the testing data for all flow parameters taken into consideration

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