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CLC number: TP273; TM911.4
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2009-10-18
Cited: 1
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Predictive control of a direct internal reforming SOFC using a self recurrent wavelet network model
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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.
@article{title="Predictive control of a direct internal reforming SOFC using a self recurrent wavelet network model",
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",
number="1",
pages="61-70",
year="2010",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A0800887"
}
%0 Journal Article
%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
%J Journal of Zhejiang University SCIENCE A
%V 11
%N 1
%P 61-70
%@ 1673-565X
%D 2010
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A0800887
TY - JOUR
T1 - Predictive control of a direct internal reforming SOFC using a self recurrent wavelet network model
A1 - Jun LI
A1 - Nan GAO
A1 - Guang-yi CAO
A1 - Heng-yong TU
A1 - Ming-ruo HU
A1 - Xin-jian ZHU
A1 - Jian LI
J0 - Journal of Zhejiang University Science A
VL - 11
IS - 1
SP - 61
EP - 70
%@ 1673-565X
Y1 - 2010
PB - Zhejiang University Press & Springer
ER -
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.
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