基于MEVMD与GA-CNN+LSTM的NOx浓度动态预测模型研究OA

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.