Coal-fired power plant boiler is one of the major sources of SO_2 pollution emission. In circulating fluidized bed(CFB) boiler

the unstable desulfurization in the furnace leads to that the desulfurization of desulfurization tower is not timely

ultimately resulting in excessive SO_2 emission. Establishing an effective SO_2 emission prediction model can effectively solve this problem. The SO_2 emission characteristics are influenced by many thermal parameters which are correlation and coupling. In this regard

this paper presents a SO_2 emission prediction model based on variable selection and SVM. Based on the field operation data of a 300 MW CFB boiler

the BP neural network was applied to reduce the dimension and the complexity of the input variables

and the filtered variables were used as the input of BP-SVM model. And then K-fold cross validation method was used to determine the optimal parameters of the model by grid search to establish the SO_2 emissions BP-SVM model. Compared with the SVM model without variable selection

the results show that the BP-SVM model after the variable selection can effectively reduce the model complexity and improve the generalization ability.