锅炉技术2025，Vol.56Issue(2)：31-37,7.
基于DF-AdaboostSVM模型的脱硝入口氮氧化物浓度预测研究
Research on Prediction of Nitrogen Oxide Concentration at the Denitration Inlet Based on DF-AdaboostSVM Model
马立增 1张玲 1谷宇 1吴俣 2唐媛媛 2汤光华2
作者信息
- 1. 国能蚌埠发电有限公司,安徽 蚌埠 233411
- 2. 南京国电环保科技有限公司,江苏南京 210061
- 折叠
摘要
Abstract
The traditional coal-fired power unit denitrification system lacks precision in am-monia injection,resulting in excessive ammonia injection,excessive nitrogen oxide emissions,and inability to automatically inject ammonia.To solve these problems,it is nec-essary to achieve precise control of the total amount of ammonia injection and balanced pro-portion of ammonia nitrogen in the denitrification reactor space.This paper proposes a nitro-gen oxide concentration prediction model based on Dominant Factor(DF)and Adaboost inte-grated Support Vector Machine(SVM)to address the issue of inaccurate control of total am-monia injection due to the lag of nitrogen oxide concentration detection at the inlet of the denitrification system reactor.Firstly,through DF analysis of the historical operating data of a 660MW coal-fired power unit,select the auxiliary characteristic parameters that have a sig-nificant impact on the nitrogen oxide concentration at the denitrification inlet and determine the lag time of the selected parameters relative to the nitrogen oxide concentration.Then,based on the lag time reconstruction dataset,a DF-AdaboostSVM nitrogen oxide concentration prediction model is constructed.The research results indicate that compared to modeling with a limited single lag time of 180 s,240 s,and 300 s,as well as a single SVM model,building an integrated model using DF lag time reconstruction dataset has better pre-diction accuracy,with an average percentage error of 4.03％,root mean square error of 16.74,and R2 of 0.91,all of which are superior to the other models mentioned above.From this,it can be seen that the proposed algorithm and model are more suitable for predicting the nitrogen oxide concentration at the denitrification inlet.关键词
主导因素/Adaboost集成/迟滞时间/氮氧化物浓度/预测模型Key words
dominant factor/Adaboost integration/lag time/nitrogen oxide concentra-tion/prediction model分类
环境科学引用本文复制引用
马立增,张玲,谷宇,吴俣,唐媛媛,汤光华..基于DF-AdaboostSVM模型的脱硝入口氮氧化物浓度预测研究[J].锅炉技术,2025,56(2):31-37,7.
