| Power Engineering |
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| Predictive control of a direct internal reforming SOFC using a self recurrent wavelet network model |
| Jun LI, Nan GAO, Guang-yi CAO, Heng-yong TU, Ming-ruo HU, Xin-jian ZHU, Jian LI |
| Institute of Fuel Cell, Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China; Department of Mathematics, Shanghai University, Shanghai 200444, China; College of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China |
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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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Received: 31 December 2008
Published: 30 November 2009
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