中圖分類號：TP183
文獻標(biāo)識碼：A
DOI:10.19358/j.issn.2097-1788.2023.04.012
引用格式：王淵博，金秀章.基于特征優(yōu)化和ISSALSTM的脫硝系統(tǒng)入口NOx濃度預(yù)測模型［J］.網(wǎng)絡(luò)安全與數(shù)據(jù)治理，2023，42（4）：70-77，84.

Prediction model of NOx concentration at the inlet of the denitration system based on feature optimization and ISSALSTM

Abstract： Aiming at the problem that the NOx concentration at the inlet of the denitrification system in power plants is greatly affected by many factors and fluctuates greatly, and the CEMS detection instruments have great delays and are difficult to accurately measure, a prediction model for the NOx concentration at the inlet of the denitrification system based on the random deep forest algorithm (RF) and the improved sparrow search algorithm (ISSA) optimized longterm and shortterm memory neural network (LSTM) was proposed. Firstly, the initial auxiliary variables related to the mass concentration of NOx at the SCR inlet were determined by mechanism and correlation analysis, and the auxiliary variables were selected for feature optimization using the RF algorithm, then the delay between each auxiliary variable and the output variables were estimated by mutual information (MI) and the timing features were extracted, and the LSTM neural network prediction model was established by denoising the input variables through wavelet filtering. The modified sparrow search algorithm was used to determine the optimal combination parameters of the LSTM model and finally contrasted with the prediction results of the traditional LSSVM, RBF and BP models. The experimental results proved that the ISSALSTM neural network prediction model after feature optimization had the largest coefficient of determination (R2) and the smallest root mean square error (RMSE) and mean absolute percentage error (MAPE), which exhibited strong fitting and generalization ability to accurately predict the mass concentration of NOx at the inlet of the denitrification system.

Key words : NOx concentration prediction；feature optimization； mutual information； sparrow search algorithm；LSTM neural network；random forest