Abstract：The establishment of SO2 concentration prediction model at desulfurization outlet is the basis for realizing the economic operation of desulfurization system. Aiming at this problem, a SO2 concentration prediction model at desulfurization outlet based on variable selection of maximum information coefficient (MIC) and marine predation algorithm (MPA) optimized nuclear limit learning machine (KELM) was proposed. Firstly, the mechanism analysis method was used to screen the variables affecting the SO2 concentration at the outlet, and the expression method of comprehensive flow of circulating slurry was proposed to describe the influence characteristics of slurry circulating pump combination. On this basis the input variables of the model were determined by the variable selection algorithm based on the maximum information coefficient. Then, MPA was used to optimize the regularity coefficient C and nuclear parameter S of KELM, and the outlet SO2 concentration prediction model of MPA-KELM was established. Finally, the simulation experiment was carried out by using the operation data of the power plant. The results show that after variable selection, the mean square error and average absolute percentage error of MPA-KELM model are 1.23666mg/m3 and 4.9876% respectively. The prediction accuracy is high, which can provide technical support for the on-site optimal control of SO2 in the desulfurization system.
闫浩思,赵文杰. 基于MIC和MPA-KELM的脱硫出口SO2浓度预测[J]. 计量学报, 2023, 44(2): 271-278.
YAN Hao-si,ZHAO Wen-jie. Prediction of SO2 Concentration at Desulfurization Outlet Based on MIC and MPA-KELM. Acta Metrologica Sinica, 2023, 44(2): 271-278.
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2023, Vol. 44 
