中图分类号：X773
文献标识码：A
DOI:10.19358/j.issn.2097-1788.2023.09.013
引用格式：张瑾，姜浩，金秀章.基于互信息变量选择的燃煤机组SCR脱硝系统PSO-ELM建模［J］.网络安全与数据治理，2023，42（9）：88-95.

PSO-ELM modeling of SCR denitrification system of coal-fired units based on mutual information variable selection

Abstract： Aiming at the problems of NOx concentration at the outlet of selective catalytic reduction (SCR) denitration system of coal-fired units, such as measurement lag and data distortion during purging, a SCR denitration system model of coal-fired units based on feature extraction and particle swarm optimization (PSO) to optimize extreme learning machine (ELM) hyperparameters is proposed in this paper. Mutual information (MI) was used to compensate the time delay, maximum correlation minimum redundancy (mRMR) was used to screen the auxiliary variables, and the optimal ELM hyperparameters were determined by PSO optimization algorithm and the prediction model was established. Finally, the comparison and verification were carried out. The simulation results show that the mean square error and correlation coefficient of the PSO-ELM prediction model established by the method in this paper are 0.931 4 mg/m3 and 0.978 6 respectively, with high prediction accuracy, which can provide technical support for the on-site optimization control of NOx at the exit of the denitrification system.

Key words : mutual information；PSO algorithm；SCR-DeNOx system；extreme learning