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Technical Briefs

Modeling of an Intermediate Temperature Solid Oxide Fuel Cell Using the Adaptive Neuro-Fuzzy Inference System (ANFIS)

[+] Author and Article Information
Yasemin Vural1

Centre for Computational Fluid Dynamics, University of Leeds, Leeds LS2 9JT, UKpmyv@leeds.ac.uk

Diyar Akay

Department of Industrial Engineering, Gazi University, Ankara 06570, Turkey

Mohamed Pourkashanian, Derek B. Ingham

Centre for Computational Fluid Dynamics, University of Leeds, Leeds LS2 9JT, UK

1

Corresponding author.

J. Fuel Cell Sci. Technol 7(3), 034501 (Mar 12, 2010) (4 pages) doi:10.1115/1.3207873 History: Received August 14, 2008; Revised May 13, 2009; Published March 12, 2010; Online March 12, 2010

The modeling of an intermediate temperature solid oxide fuel cell using an adaptive neuro-fuzzy inference system (ANFIS) has been presented. The results show that a well trained and well tested ANFIS model has the capability to predict the fuel cell performance under varying operational conditions depending on the availability of the data and can be used as an alternative to the physical models in the sense that the results can be produced in a fast and cost effective way. The performance of the model in regions where there is data deficiency has been discussed.

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Figures

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Figure 1

Training data versus ANFIS output

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Figure 2

Test data versus ANFIS output

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Figure 3

Current-voltage curve as a function of fuel mass flow rate: volumetric H2 content is 53% and temperature is 800°C

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Figure 4

Current-voltage curve as a function of volumetric H2 rate (%): mass flow inlet rate is 0.00756 g/s and temperature is 800°C

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