Quantitative analysis of the content of nitrogen and sulfur in coal based on laser-induced breakdown spectroscopy: effects of variable selection

Coal is a crucial fossil energy in today’s society, and the detection of sulfur (S) and nitrogen (N) in coal is essential for the evaluation of coal quality. Therefore, an efficient method is needed to quantitatively analyze N and S content in coal, to achieve the purpose of clean utilization of coal. This study applied laser-induced breakdown spectroscopy (LIBS) to test coal quality, and combined two variable selection algorithms, competitive adaptive reweighted sampling (CARS) and the successive projections algorithm (SPA), to establish the corresponding partial least square (PLS) model. The results of the experiment were as follows. The PLS modeled with the full spectrum of 27,620 variables has poor accuracy, the coefficient of determination of the test set (R²P) and root mean square error of the test set (RMSEP) of nitrogen were 0.5172 and 0.2263, respectively, and those of sulfur were 0.5784 and 0.5811, respectively. The CARS-PLS screened 37 and 25 variables respectively in the detection of N and S elements, but the prediction ability of the model did not improve significantly. SPA-PLS finally screened 14 and 11 variables respectively through successive projections, and obtained the best prediction effect among the three methods. The R²P and RMSEP of nitrogen were 0.9873 and 0.0208, respectively, and those of sulfur were 0.9451 and 0.2082, respectively. In general, the predictive results of the two elements increased by about 90% for RMSEP and 60% for R2P compared with PLS. The results show that LIBS combined with SPA-PLS has good potential for detecting N and S content in coal, and is a very promising technology for industrial application.