NO_X Concentration Prediction at Secondary Combustion Chamber Outlet in Small-Scale Domestic Waste Incineration System

Small-scale municipal solid waste incineration systems (distributed waste treatment technology) have great potential. Yet, secondary combustion is highly sensitive to pyrolysis furnace outlet parameter fluctuations, causing significant NO_X fluctuations at the secondary combustion chamber outletcomplicating monitoring, impairing stability, limiting emission performance, and hindering application. To address these challenges, this study proposes a soft sensing method using a Least Squares Support Vector Machine (MIC-ISSA-LSSVM) optimized with an Improved Sparrow Search Algorithm for predicting NO_X concentration. Key variables influencing NO_X concentration were identified through maximum mutual information (MIC) dimensionality reduction and Spearman correlation coefficient screening and were subsequently used to train the ISSA-LSSVM model. Compared with the ISSA-LSSVM model, the proposed MIC-ISSA-LSSVM model achieved an R² improvement of 1.84%, a RMSE reduction of 17.72%, and a mean absolute error (MAE) reduction of 17.89%. Compared with the MIC-SVM, MIC-LSSVM, MIC-BP, MIC-RBF, MIC-CNN, and MIC-LSTM, the MIC-ISSA-LSSVM model achieved R² improvements of 0.46%, 0.44%, 13.97%, 8.39%, 8.90%, and 6.10%, respectively; RMSE reductions of 5.51%, 5.36%, 51.37%, 42.64%, 43.52%, and 36.93%, respectively; and MAE reductions of 9.00%, 4.05%, 51.84%, 40.37%,42.90%, and 35.00%, respectively. In summary, the MIC-ISSA-LSSVM model showed strong performance in predicting secondary combustion chamber outlet parameters, offering valuable reference for its design and potential application to other large/medium incinerators and related equipment.