Abstract：In order to improve the emission control level of SO2 pollutants in thermal power plants, a combined prediction model of SO2 concentration at outlet of absorber was proposed based on VMD-IASO-ELM. Firstly, the mechanism and correlation analysis were used to determine the initial correlation variables of SO2 concentration at outlet of absorber, and variational mode decomposition (VMD) algorithm was used to decompose it. The low-frequency components with large mutual information between the decomposition results and the output were retained. Then, the simple structure was established based on fast-learning extreme learning machine (ELM), and the improved atomic search optimization (IASO) based on hybrid strategy was used to optimize network parameters and improve the prediction accuracy. Finally, the error correction term was deduced from fuzzy rules to correct the prediction results of ELM model. Using historical data for simulation modeling, the results show that the model has high prediction accuracy and learning ability and can accurately track the change trend of SO2 concentration at the outlet of absorber.
金秀章,李阳峰,姚宁. 基于VMD-IASO-ELM的吸收塔出口SO2浓度组合预测模型[J]. 计量学报, 2023, 44(4): 630-637.
JIN Xiu-zhang,LI Yang-feng,YAO Ning. Combined Prediction Model of SO2 Concentration at Outlet of Absorber Based on VMD-IASO-ELM. Acta Metrologica Sinica, 2023, 44(4): 630-637.
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2023, Vol. 44 
