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CLC number: TP273; TM911.4

On-line Access: 2009-11-30

Received: 2008-12-31

Revision Accepted: 2009-03-12

Crosschecked: 2009-10-18

Cited: 1

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Journal of Zhejiang University SCIENCE A 2010 Vol.11 No.1 P.61-70

http://doi.org/10.1631/jzus.A0800887


Predictive control of a direct internal reforming SOFC using a self recurrent wavelet network model


Author(s):  Jun LI, Nan GAO, Guang-yi CAO, Heng-yong TU, Ming-ruo HU, Xin-jian ZHU, Jian LI

Affiliation(s):  Institute of Fuel Cell, Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China; more

Corresponding email(s):   jun.li.fc@gmail.com

Key Words:  Direct internal reforming (DIR), Solid oxide fuel cell (SOFC), Predictive control, Self recurrent wavelet network (SRWN)


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Jun LI, Nan GAO, Guang-yi CAO, Heng-yong TU, Ming-ruo HU, Xin-jian ZHU, Jian LI. Predictive control of a direct internal reforming SOFC using a self recurrent wavelet network model[J]. Journal of Zhejiang University Science A, 2010, 11(1): 61-70.

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author="Jun LI, Nan GAO, Guang-yi CAO, Heng-yong TU, Ming-ruo HU, Xin-jian ZHU, Jian LI",
journal="Journal of Zhejiang University Science A",
volume="11",
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pages="61-70",
year="2010",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A0800887"
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%T Predictive control of a direct internal reforming SOFC using a self recurrent wavelet network model
%A Jun LI
%A Nan GAO
%A Guang-yi CAO
%A Heng-yong TU
%A Ming-ruo HU
%A Xin-jian ZHU
%A Jian LI
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%I Zhejiang University Press & Springer
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A1 - Ming-ruo HU
A1 - Xin-jian ZHU
A1 - Jian LI
J0 - Journal of Zhejiang University Science A
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%@ 1673-565X
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.A0800887


Abstract: 
In this paper, an application of a nonlinear predictive controller based on a self recurrent wavelet network (SRWN) model for a direct internal reforming solid oxide fuel cell (DIR-SOFC) is presented. As operating temperature and fuel utilization are two important parameters, the SOFC is identified using an SRWN with inlet fuel flow rate, inlet air flow rate and current as inputs, and temperature and fuel utilization as outputs. To improve the operating performance of the DIR-SOFC and guarantee proper operating conditions, the nonlinear predictive control is implemented using the off-line trained and on-line modified SRWN model, to manipulate the inlet flow rates to keep the temperature and the fuel utilization at desired levels. Simulation results show satisfactory predictive accuracy of the SRWN model, and demonstrate the excellence of the SRWN-based predictive controller for the DIR-SOFC.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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