基于互信息PSO-LSSVM的SO2浓度预测

doi:10.3969/j.issn.1000-1158.2021.05.20

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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.

Key words： metrology SO2 concentration prediction mutual information PSO LSSVM mRMR

Received: 15 October 2019 Published: 24 May 2021

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TB99

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	JIN Xiu-zhang
	LI Jing

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JIN Xiu-zhang,LI Jing. Prediction of SO₂ Concentration Based on Mutual Information PSO-LSSVM[J]. Acta Metrologica Sinica, 2021, 42(5): 675-680.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2021.05.20 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2021/V42/I5/675

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