In order to better reflect the relationship between the wet flue gas desulfurization process parameters and the desulfurization efficiency

the partial least squares regression(PLS) method was applied to analyze the process factors affecting the flue gas desulfurization efficiency

then the factors affecting the desulfurization efficiency greatly were considered as the principal components and were extracted for prediction using the support vector machine(SVM). Thus

on the basis of the partial least squares support vector machine(PLS-SVM)

a prediction model for wet flue gas desulfurization efficiency was established

which can reduce the input dimensions of the SVM. Moreover

the data of the operation monitoring system of the limestone gypsum wet desulphurization facility were selected to carry out the training and prediction for the model. The prediction results show that

the maximum absolute prediction error of the PLS-SVM model is below 0.65%

and the average absolute error is about 0.3%

indicating the above model can well predict the desulfurization efficiency

and its prediction efficiency and accuracy is higher than that of the SVM model. In order to better reflect the relationship between the wet flue gas desulfurization process parameters and the desulfurization efficiency

the partial least squares regression(PLS) method was applied to analyze the process factors affecting the flue gas desulfurization efficiency

then the factors affecting the desulfurization efficiency greatly were considered as the principal components and were extracted for prediction using the support vector machine(SVM). Thus

on the basis of the partial least squares support vector machine(PLS-SVM)

a prediction model for wet flue gas desulfurization efficiency was established

which can reduce the input dimensions of the SVM. Moreover

the data of the operation monitoring system of the limestone gypsum wet desulphurization facility were selected to carry out the training and prediction for the model. The prediction results show that

the maximum absolute prediction error of the PLS-SVM model is below 0.65%

and the average absolute error is about 0.3%

indicating the above model can well predict the desulfurization efficiency

and its prediction efficiency and accuracy is higher than that of the SVM model.