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- W2389891123 abstract "Direct internal reforming solid oxide fuel cell(DIR-SOFC) is an electric energy generation device which is directly fueled with hydrocarbons and converts the chemical energy of the fuel directly into electrical energy.Wavelet neural network(WNN) combines the advantage of multi-resolution approximation(MRA) of the wavelet decomposition and the capability of neural networks in learning from nonlinear process.In order to avoid considering the complexity of reactions and the coupling parameters inside the DIR-SOFC,the WNN,optimized by the immune algorithm,is used as an identification tool to establish a dynamic model of the DIR-SOFC with the inlet fuel flow rate,the inlet air flow rate and load current as inputs and the voltage and temperature as predictive outputs.Furthermore,the comparisons of the results between the immune-optimized WNN model and a RBF neural network(RBFNN) model are presented.The simulation results show that,by using the presented model,the absolute values of relative errors of the predictive voltage and temperature are less than 0.0007 and 0.0004,respectively,and the mean absolute values of relative errors are 0.0003 and 0.0002,respectively.The obtained dynamic model can effectively simulate the dynamics of the DIR-SOFC with relatively high accuracy." @default.
- W2389891123 created "2016-06-24" @default.
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- W2389891123 date "2010-01-01" @default.
- W2389891123 modified "2023-09-24" @default.
- W2389891123 title "Dynamic Modeling of DIR-SOFC Using Wavelet Neural Networks Based on Immune Algorithm" @default.
- W2389891123 hasPublicationYear "2010" @default.
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