华南理工大学学报（自然科学版）2025，Vol.53Issue(7)：1-10,10.DOI:10.12141/j.issn.1000-565X.240519
基于MIC-PCA-LSTM模型的垃圾焚烧炉NOx排放浓度预测
Emission Concentration Prediction of NOx from Waste Incinerator Based on MIC-PCA-LSTM Model
摘要
Abstract
Accurately predicting the emission concentration of NOx at the outlet of the selective catalytic reduction(SCR)denitrification system in the waste incineration process is of great significance for enhancing data quality and optimizing ammonia injection.However,the waste incineration process exhibits significant nonlinearity,multivari-ate coupling,and time-series characteristics.These factors pose substantial challenges to achieving accurate predic-tion of NOx emissions.To solve this problem,this paper presents a prediction model for the emission concentration of NOx at the outlet of SCR denitrification system by integrating maximum information coefficient(MIC),principal component analysis(PCA)and long short-term memory(LSTM)neural networks.First,MIC is employed to assess the maximum normalized mutual information values among variables,and the input variables that exhibit the strong-est correlation with NOx emission concentration are selected while the redundant variables are eliminated based on the principle of maximum redundancy.Then,PCA is utilized to obtain the cumulative contribution rate of the va-riance of each principal component,extract the principal component features,and obtain the optimal input feature variable set.Finally,an emission prediction model of NOx at the outlet of SCR denitrification system is established based on the LSTM neural network.The results indicate that,as compared with the back propagation neural net-work model and the support vector machine model,the proposed model exhibits higher accuracy and generalization ability,achieving a mean absolute percentage error of 6.33%,a root mean squared error of 4.71 mg/m3 and a deter-mination coefficient of 0.90.This research lays a theoretical foundation for achieving the intelligent control of SCR denitrification system in the waste incineration process.关键词
垃圾焚烧/选择性催化还原/排放浓度预测/最大信息系数/主成分分析/长短期记忆神经网络Key words
waste incineration/selective catalytic reduction/emission concentration prediction/maximum information coefficient/principal component analysis/long short-term memory neural network分类
信息技术与安全科学引用本文复制引用
姚顺春,李龙千,刘文,李峥辉,周安鹂,李文静,陈姜宏,卢志民..基于MIC-PCA-LSTM模型的垃圾焚烧炉NOx排放浓度预测[J].华南理工大学学报（自然科学版）,2025,53(7):1-10,10.基金项目
国家重点研发计划项目(2024YFC3909002) （2024YFC3909002）
国家重点研发计划子课题项目(2024YFC3909004-02) （2024YFC3909004-02）
广东省能源高效清洁利用重点实验室项目(2013A061401005)Supported by the National Key Research and Development Program of China(2024YFC3909002)and the Subproject of the National Key Research and Development Program of China(2024YFC3909004-02) （2013A061401005）
