Prediction of Coal Ash Fusion Temperature by Least-Squares Support Vector Machine Model

Coal ash fusion temperature (AFT) is one important parameter for coal-fired boiler design and evaluation in a power plant. The relationship between coal AFT and the chemical composition of coal ash is rather complex in nature and makes the modeling of AFT difficult. In this work, a least-squares support vector machine (LS-SVM) model, which was based on a dynamically optimized search technique with cross-validation, is developed to predict the coal ash softening temperature (ST). The accuracy of this LS-SVM model was verified by comparison with the experimental AFT data of different types of coal. Further, the comparison of the present LS-SVM model and the traditional models, for example, multilinear regressions (MLR) and multi-nonlinear models (MNR) as well as the artificial neural network (ANN) models, showed that the LS-SVM model was much better to provide the highest generalized accuracy with the mean squared error of 0.0128 and correlation coefficient of 0.9272. Furthermore, based on the LS-SVM model, the correlativity between coal ash composition and the ST was analyzed, which helped us deeply understand how the parameters influenced the fusion behavior of coal ash.