Abstract：Aiming at the problem of SO2 pollutant emission in thermal power plant, a PSO-LSSVM model prediction method was proposed based on the mutual information.The auxiliary variables of high correlation with the measured inlet concentration of SO2 were selected as the input of the model to realize the prediction of the dominant variable SO2 concentration.The auxiliary variables screened by mutual information had the higher correlation with the auxiliary variables selected by mechanism analysis and Pearson correlation. The auxiliary variables selected by mutual information were used as the input of the LSSVM model and the particle swarm optimization (PSO) was used to determine the parameters of the LSSVM not only reduced the calculation time, but also improved the prediction accuracy. The method was applied to the soft measurement of SO2 concentration in a thermal power plant, and the simulation was carried out by using field data, which showed that the prediction accuracy was higher, the relative error was lower, the prediction trend was closer to the actual value, and the error between the actual value and the predicted value was reduced (the square root error is 2.485 and the average relative error is 0.2603%). It provided the software technical support for the on-site SO2 concentration advance control.
金秀章,李京. 基于互信息PSO-LSSVM的SO2浓度预测[J]. 计量学报, 2021, 42(5): 675-680.
JIN Xiu-zhang,LI Jing. Prediction of SO2 Concentration Based on Mutual Information PSO-LSSVM. Acta Metrologica Sinica, 2021, 42(5): 675-680.
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2021, Vol. 42 
