哈尔滨商业大学学报（自然科学版）2026，Vol.42Issue(2)：195-202,8.
基于MI-IDBO-LSTM的SCR脱硝系统出口NOx体积分数预测
Prediction of NOx concentration at outlet of SCR denitration system based on MI-IDBO-LSTM
摘要
Abstract
To address the issue of large prediction errors in SCR outlet NOx concentration caused by significant time delays and multiple disturbances in coal-fired power plant SCR denitrification systems,a prediction model of NOx concentration at the outlet of SCR based on MI-IDBO-LSTM was proposed.The time delay estimation of each input variable was completed by using Mutual Information(MI),and the time series of the data was reconstructed.Based on the processed data,the prediction model was established by using LSTM.The dung beetle optimizer(DBO)was improved by introducing the Tent chaotic map,adaptive weight,and the secretary bird optimization algorithm integrated with adaptive weight,so as to enhance its optimization ability.IDBO was used to optimize the key parameters of LSTM to improve the prediction accuracy of the model.Based on the historical operation data of SCR denitration system in a domestic 350 MW coal-fired power plant,the simulation results of IDBO-LSTM were compared with those of LSTM and DBO-LSTM.The results showed that IDBO-LSTM achieved the optimal performance among all models,with a mean absolute error of 0.453,a coefficient of determination of 0.976,and a root mean square error of 0.621.Experiments showed that the prediction model based on MI-IDBO-LSTM could achieve accurate prediction.关键词
SCR脱硝/时延估计/改进蜣螂算法/长短期记忆网络/预测模型Key words
SCR denitration/time delay estimation/improved dung beetle optimizer/long short-term memory/prediction model分类
能源科技引用本文复制引用
陈静,朱龙祥..基于MI-IDBO-LSTM的SCR脱硝系统出口NOx体积分数预测[J].哈尔滨商业大学学报（自然科学版）,2026,42(2):195-202,8.基金项目
国家自然科学基金项目(51874010) （51874010）
安徽省教育厅高校自然科学研究项目(KJ2018A0087). （KJ2018A0087）
