Abstract
AbstractCoal's calorific value and carbon content are crucial in calculating carbon emissions. Accurately detecting these two indicators is of great significance for carbon accounting. In this study, we developed a compact coal quality rapid detection integrated machine based on laser‐induced breakdown spectroscopy (LIBS), which can directly measure coal particle flow. A partial least squares model, based on data set selection according to cluster analysis results, was applied to establish the relationship between coal quality and plasma spectra. The R2 of the calorific value is .93, the root mean square error of prediction (RMSEP) is 0.41 MJ/kg, and the mean absolute error (MAE) is 0.33 MJ/kg. The R2 of carbon content is .94, the RMSEP is 0.97%, and the MAE is 0.91%. These results indicated that the developed compact coal quality rapid detection integrated machine could conduct precise coal quality analysis in real time.
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