电力科技与环保2024，Vol.40Issue(3)：313-320,8.DOI:10.19944/j.eptep.1674-8069.2024.03.011
基于CIFE-FOA-DELM的SCR脱硝入口NOx浓度预测方法研究
A CIFE-FOA-DELM method for predicting NOx concentration at the inlet of SCR denitration system
摘要
Abstract
Aiming at the lag problem of ammonia injection control caused by the monitoring value of denitrification inlet NOx concentration as the feed-forward input of denitrification,the CIFE-FOA-DELM prediction method of denitrification inlet NOx concentration based on furnace parameters is proposed. A mutual information feature selection method is used to select feature variables for the prediction model;deep limit learning optimised by Drosophila optimisation algorithm is introduced to establish the NOx concentration prediction model;and the model is validated by using the historical operation data of a 660 MW thermal power unit,and the prediction results are compared with those of the back-propagation,support vector machine,deep limit learning machine,and FOA-SVM models. The results show that the CIFE-FOA-DELM prediction method has higher prediction accuracy,and the mean absolute percentage error (SMAPE),the root mean square error (SRMSE),and the goodness of fit (R2) are 0.261％,1.384％,and 0.965％,respectively,and the prediction of the denitrification inlet NOx concentration is 180 s ahead of schedule when compared with the CEMS data,which is conducive to solving the ammonia injection control lag problem. The problem of ammonia injection control lag is solved.关键词
SCR/脱硝入口NOx浓度/CIFE-FOA-DELM/互信息特征选择/果蝇优化算法/深度极限学习机/喷氨控制Key words
SCR/NOx concentration at the denitrification inlet/cife-foa-delm/mutual information feature selection/drosophila optimization algorithm/deep extreme learning machine/ammonia injection control分类
能源科技引用本文复制引用
董威,林子杰,王雅昀..基于CIFE-FOA-DELM的SCR脱硝入口NOx浓度预测方法研究[J].电力科技与环保,2024,40(3):313-320,8.基金项目
国家重点研发计划(2022YFC3701504) （2022YFC3701504）
