Machine Learning Prediction of NO_x Emissions from Sewage Sludge Incineration

Accurate prediction of NO_x emissions is critical for optimizing selective non-catalytic reduction systems in circulating fluidized bed boilers co-firing sewage sludge and coal. This study develops a data-driven framework by using operational records. A multidimensional feature engineering scheme incorporating auto-regressive, combustion-related, denitration-related features is constructed. Six models-linear regression, random forest, XGBoost, backpropagation neural network, tuned XGBoost, and a stacking ensemble-are systematically compared. SHAP analysis reveals that the model correctly learns the key physicochemical mechanisms, including the temperature window effect and the reducing role of ammonia. The results provide a reliable predictive basis for precise ammonia injection control under fluctuating sludge incineration conditions.