基于近红外-X射线融合光谱与非线性残差校正的大粒度煤热值PLS-AE-RR预测模型OA

To address the enhanced matrix effects and intensified nonlinear spectral responses caused by the difficulty of grinding large-particle coal in industrial settings,this study proposes a hybrid PLS-AE-RR predictive model based on the fusion of near-infrared spectroscopy(NIRS)and X-ray fluorescence(XRF)spectra,aimed at improving the accuracy of on-line calorific-value analysis.The method implements a three-stage hybrid framework--linear baseline+nonlinear feature extraction+residual correction--where partial least squares regression(PLS)first models the global linear relationship between the fused spectra and calorific value;an autoencoder(AE)then extracts low-dimensional nonlinear representations that PLS cannot capture;and finally ridge regression(RR)fits and corrects the nonlinear residuals.Exper-imental validation using 153 blended coal samples from power plants demonstrates breakthrough perfor-mance in calorific value prediction for large-particle-size coal.On the test set,determination coefficients(R²)for lignite and bituminous coal reached 0.974 and 0.938,respectively,with mean absolute errors of 0.233 MJ/kg and 0.216 MJ/kg.The proposed method significantly outperforms standalone PLS and al-ternative nonlinear correction models,confirming the generalization advantage of ridge regression in residu-al fitting.Consequently,this achievement provides a grinding-free,high-precision online analysis solution for raw coal calorific value in coal-fired power plants,offering critical technical support for refined fuel man-agement and operational optimization.