Comparative Study of Four Chemometric Methods for the Quantitative Analysis of the Carbon Content in Coal by Laser-Induced Breakdown Spectroscopy Technology
Qi Ni, Yanqun Zhu, Wenyu Zhu, Yong He, Zhihua Wang
Abstract
Coal is a heterogeneous mineral substance mainly composed of carbon, along with various amounts of other elements. The carbon content is an important and pertinent parameter for coal quality. To achieve the rapid and accurate online measurement of the carbon content in coal, four different calibration strategies are applied to coal analysis by laser-induced breakdown spectroscopy (LIBS). Four calibration models based on support vector regression (SVR), back-propagation training (BP), random forest (RF), and partial least-squares regression (PLSR) were proposed, and the prediction accuracy, prediction precision, model stability, and training velocity of the four calibration models were compared for the quantitative analysis of the carbon content. A total of 65 coal samples were ablated, and the plasma spectra were used as the input data. Among the four calibration models, the results indicate that SVR and BP are the most promising calibration models for finding a better optimized model with a better prediction accuracy and prediction precision, and PLSR has a better prediction stability and a faster training velocity; however, RF has a prediction performance worse than those of the other three models.