• • The TCN model achieved the lowest MAE of 0.505, indicating superior fitting accuracy for coal property prediction, which translates to reduced error in real-time quality assessment for power plant combustion optimization.
• • The RF model attained the lowest RMSE of 0.618, demonstrating minimal deviation between predicted and actual values, ensuring reliable quality control in coal blending and supply chain management.
• • The TCN model exhibited the lowest CV of 0.042, signifying high consistency and generalization across test sets, which is critical for deploying models across diverse coal sources without recalibration.
• • SHAP analysis identified peak 52 (1141.35–1157.65 nm) as the most influential across all models, with specific peaks for ash (peak 46: 1173.89–1179.23 nm), moisture (peak 18: 1382.19–1388.10 nm), and fixed carbon (peaks 5 and 28: 1914.79–1920.46 nm and 1267.38–1270.48 nm), enabling targeted spectral feature selection for enhanced model interpretability and potential sensor design.