Rapid Quantitation of Coal Proximate Analysis by Using Laser-Induced Breakdown Spectroscopy

Abstract

Proximate analysis of coal is of great significance to ensure the safe and economic operation of coal-fired and biomass-fired power generation units. Laser-induced breakdown spectroscopy (LIBS) assisted by chemometric methods could realize the prediction of coal proximate analysis rapidly, which makes up for the shortcomings of the traditional method. In this paper, three quantitative models were proposed to predict the proximate analysis of coal, including principal component regression (PCR), artificial neural networks (ANNs), and principal component analysis coupled with ANN (PCA-ANN). Three model evaluation indicators, such as the coefficient of determination (R2), root-mean-square error of cross-validation (RMSECV), and mean square error (MSE), were applied to measure the accuracy and stability of the models. The most accurate and stable prediction of coal proximate analysis was achieved by PCR, of which the average R2, RMSECV, and MSE values were 0.9944, 0.39%, and 0.21, respectively. Although the R2 values of ANN and PCA-ANN were greater than 0.9, the higher RMSECV and MSE values indicated that ANN and PCA-ANN were inferior to PCR. Compared with the other two models, PCR could not only achieve accurate prediction, but also shorten the modeling time.