A hybrid model for furnace exit gas temperature monitoring based on CM-LSSVM-PLS

Monitoring system of furnace ash fouling is the foundation of the soot-blowing operation on furnace area. For furnace exit gas temperature (FEGT) is the key parameter in monitoring system, a new CM-LSSVM-PLS method is proposed to predict FEGT. In the process of CM-LSSVM-PLS method, considering the characteristics of operational data, c-means (CM) cluster algorithm is used to partition the training data into several different subsets. Submodels are subsequently developed in the individual subsets based on least squares support vector machine (LSSVM). Finally, partial least squares (PLS) algorithm is employed as the combination strategy. The single LSSVM is established to make a comparison with CM-LSSVM-PLS method. The proposed model is verified through operation data of a 300MW generating unit. The comparison result shows that the new CM-LSSVM-PLS method can predict FEGT accurately while the time consumed in modeling decrease drastically.