新疆大学学报（自然科学版中英文）2026，Vol.43Issue(2)：169-182,14.DOI:10.13568/j.cnki.651094.651316.2025.12.05.0001
基于MEVMD与GA-CNN+LSTM的NOx浓度动态预测模型研究
A Dynamic Prediction Model for NOx Concentration Based on MEVMD and GA-CNN+LSTM
摘要
Abstract
Coal-fired power plants serve as primary sources of NOx emissions,and the efficient operation of SCR denitrifica-tion systems is crucial for reducing pollutant emissions.However,the highly dynamic changes of data during NOx prediction processes limit the accuracy of predictive models.Therefore,a hybrid prediction framework based on modal energy diffe-rence and sample entropy,which combining variational mode decomposition(MEVMD)with genetic algorithm(GA)to opti-mize convolutional neural network(CNN)and long short-term memory network(LSTM),is proposed.Firstly,abnormal data are corrected using the 3σ criterion;20 key auxiliary variables are selected via Pearson correlation coefficients.The maximum information coefficient(MIC)is employed to determine the delay time for each variable,achieving temporal alignment be-tween features and target variables.Secondly,adaptive variational mode decomposition(VMD)precisely extracts multi-frequency features from NOx time-series signals.Hyperparameters are optimized via GA to achieve adaptive modeling of mul-tiple sub-modes.Finally,prediction results are generated through data reconstruction.Experimental results demonstrate that the proposed model achieves RMSE of 0.949 2,MAE of 0.496 9,and R2 of 0.976 7,outperforming comparison models.关键词
NOx预测/自适应变分模态分解/遗传算法/卷积神经网络/长短期记忆网络Key words
NOx prediction/adaptive variational mode decomposition/genetic algorithm/convolutional neural network/long short-term memory network分类
能源科技引用本文复制引用
张启帆,胡丽娜,曾浩,刘威,杨灿..基于MEVMD与GA-CNN+LSTM的NOx浓度动态预测模型研究[J].新疆大学学报（自然科学版中英文）,2026,43(2):169-182,14.基金项目
新疆维吾尔自治区科技重大专项"燃煤锅炉变负荷运行智能控制技术研究"(2023A01005-1) （2023A01005-1）
国家重点研发项目"全烧高碱煤锅炉多目标深度优化变负荷运行控制技术"(2023YFB4102704-01) （2023YFB4102704-01）
新疆碳中和能源科学与技术研究"天山英才"培养计划(2022TSYCLJ0001). （2022TSYCLJ0001）
