Explainable ANN Modeling of HCl and HF Emissions from Thermal Power Plant Based on Experimental Investigation

Coal combustion in large-scale power plants is a major source of atmospheric pollution, including SO2, NOx, particulate matter, and the halogen acids HCl and HF. Predicting HCl and HF emissions is challenging due to interactions among fuel composition, fly ash chemistry, combustion conditions, and flue gas dynamics. In this study, artificial neural network (ANN) models are developed from field experiments at the lignite-fired TPP “Kostolac B”. The models incorporate operational parameters (flue gas temperature and flow rate) and fuel/ash characteristics (moisture and total sulphur in coal and CaO content in ash) to estimate HCl and HF emissions. SHAP analysis identified key variables affecting halogen acid release. The developed ANN models achieved satisfactory predictive accuracy, with the test-set performances of RMSE = 2.05 mg/Nm3, R2 = 0.80, and MAPE = 18.7% for HCl prediction, and RMSE = 3.23 mg/Nm3, R2 = 0.83, and MAPE = 18.7% for HF prediction. SHAP analysis indicated that CaO content in fly ash and coal moisture are the primary drivers of HCl and HF emissions, while operating conditions and coal sulphur content influence emissions through non-linear interaction effects. The proposed ANN-SHAP framework provides a data-driven approach for emission prediction and interpretation, supporting decision-making in emission management.