A model combining spectrum standardization and dominant factor based partial least square method for carbon analysis in coal using laser-induced breakdown spectroscopy

Abstract

Quantitative measurement of carbon content in coal is essentially important for coal property analysis. However, quantitative measurement of carbon content in coal using laser-induced breakdown spectroscopy (LIBS) suffered from low measurement accuracy due to measurement uncertainty as well as the matrix effects. In this study, our previously proposed spectrum standardization method and dominant factor based partial least square (PLS) method were combined to improve the measurement accuracy of carbon content in coal using LIBS. The combination model utilized the spectrum standardization method to accurately calculate dominant carbon concentration as the dominant factor, and then applied PLS with full spectrum information to correct residual errors. The combination model was applied to measure the carbon content in 24 bituminous coal samples. Results demonstrated that the combination model can further improve measurement accuracy compared with the spectrum standardization model and the dominant factor based PLS model, in which the dominant factor was calculated using traditional univariate method. The coefficient of determination, root-mean-square error of prediction, and average relative error for the combination model were 0.99, 1.63%, and 1.82%, respectively. The values for the spectrum standardization model were 0.90, 2.24%, and 2.75%, respectively, whereas those for the dominant factor based PLS model were 0.99, 2.66%, and 3.64%, respectively. The results indicate that LIBS has great potential to be applied for the coal analysis.