Comparative study of regression modeling methods for online coal calorific value prediction from flame radiation features

In this paper, multiple regression methods are presented and compared for online coal calorific value prediction from multi-spectral flame radiation features. Several statistical approaches including principle component analysis (PCA), independent component analysis (ICA) and partial least squares analysis (PLSA) were used in linear and nonlinear regression analyses. Analyzing results show that nonlinear regression model can better approximate the relationship between the coal calorific value and the flame radiation features than linear regression model. In linear regression analysis, the performance of the linear coal calorific value prediction models was not improved by involving the statistical approaches. In nonlinear regression analysis, however, the performance of the prediction models was significantly improved when combined with the statistical approaches. The variation of coefficients of multiple regression showed that only the PLSA-based nonlinear regression model can discriminate useful feature components from useless feature components. The PLSA-based nonlinear regression model showed the best performance for coal calorific value prediction with the number of features reduced to about a third of that in the other models. With the PLSA-based nonlinear regression model, online coal calorific value prediction from the multi-band flame radiation features under the operating conditions used by the industrial boiler has the mean absolute error, standard deviation of the absolute errors, mean relative error and standard deviation of the relative errors of 148.76 kJ/kg, 291.86 kJ/kg, 0.76% and 1.53%, respectively.