Neural network predictive control of a SOFC fuelled with ammonia

Hajimolana, S.A. and Hussain, M.A. and Daud, W.M.A.W. and Chakrabarti, M.H. (2012) Neural network predictive control of a SOFC fuelled with ammonia. International Journal of Electrochemical Science, 7 (4). pp. 3737-3749. ISSN 1452-3981

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Abstract

The dynamic behavior and control of a tubular solid oxide fuel cell will be studied in this paper. The effect of fuel/air temperature and pressure will be investigated. Controlling the average stack temperature is the final objective of this study due to a high operating temperature of the system. In this case, temperature fluctuation induces thermal stress in the electrodes and electrolyte ceramics; therefore, the cell temperature distribution should be kept as constant as possible. A mathematical modeling based on first principles is developed. The fuel cell is divided into five subsystems and the factors such as mass/energy/momentum transfer, diffusion through porous media, electrochemical reactions, and polarization losses inside the subsystems are presented. Dynamic fuel-cell-tube temperature responses of the cell to step changes in conditions of the feed streams will be presented. A neural network predictive controller (NNPC) is then implemented to control the cell-tube temperature through manipulation of the temperature of the inlet air stream. The results show that the control system can successfully reject unmeasured step changes (disturbances) in the load resistance.

Item Type: Article
Additional Information: 947FF Times Cited:0 Cited References Count:33
Uncontrolled Keywords: Ammonia fuel, Neural network predictive control, Sofc, Cell-tube temperature, Proton conducting electrolyte, Finite-volume, Cell, Model, Performance, Sensitivity, Dynamics, System.
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TP Chemical technology
Divisions: Faculty of Engineering
Depositing User: Mr Jenal S
Date Deposited: 10 Jul 2013 01:05
Last Modified: 08 Nov 2017 08:44
URI: http://eprints.um.edu.my/id/eprint/7007

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