Optimizing analysis of coal property using laser-induced breakdown and near-infrared reflectance spectroscopies

Coal properties have different correlations with elements or molecules. It is difficult to optimize the analysis of multiple coal properties simultaneously by a single analytical technique. This paper reports a method for optimizing analysis of coal properties by using laser-induced breakdown spectroscopy (LIBS) and near-infrared reflectance spectroscopy (NIRS). Briefly, LIBS, NIRS, as well as spectral information fusion of LIBS and NIRS (LIBS&NIRS) were used to establish the quantitative analysis models of coal properties with partial least squares (PLS) method. The performance of models based on different spectral information was compared with each other according to the determination coefficient (R²), root mean square error of prediction (RMSEP), average absolute error (AAE), and average relative error (ARE). As a result, the models of calorific value and volatile matter based on LIBS&NIRS have the best performance with minimum root mean square error for prediction (RMSEP) of 0.192 MJ/kg and 0.672%. However, for the model of ash content, the minimum RMSEP of 0.774% was achieved by using LIBS. Meanwhile, optimal performance of modeling moisture content was obtained from NIRS with the minimum RMSEP of 0.308%. After obtaining the best prediction results of volatile matter content, ash content, and moisture content, the fixed carbon content can be calculated by the definition formula. These results demonstrated that the reported method can optimize the rapid analysis of multiple coal properties simultaneously.