DATA-DRIVEN MODELING FOR TEMPERATURE PREDICTION OF BIOMASS BURNER
Keywords:
Biomass Burner, Dynamic Modeling, Data-Driven ModelingAbstract
Biomass burner is widely used, since it is a renewable process energy eliminating agricultural wastes and reducing environmental problems. The burner temperature is an important parameter in determining the stability and efficiency of the biomass burner. Therefore, this research presents the flame temperature prediction of biomass burner using data-driven modeling. The inputs of the model were the temperature inside the gasifier, syngas temperature and the equivalence ratio. The output of the model was the flame temperature of the biomass burner. In this experiment, the predictive performance was compared between a nonlinear autoregressive network with exogenous inputs neural networks (NARXNN) and a response surface method model (RSM). From the experimental results shown that NARXNN provided better predictive results than RSM with mean squared error = 0.6859 and correlation coefficient = 0.9999 in the teaching data, = 0.7979 and = 0.9999 in the testing data. The improvement for the better prediction causes of using complicating structure of the model with the history data values of variables to increase the prediction accuracy.
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