Estimating Calorific Value of Coal Using Laser-Induced Breakdown Spectroscopy through Statistical Algorithms: Correlation Analysis, Partial Least Squares, and Signal-to-Noise Ratio

Abstract

The objective of this study was to compare different statistical algorithms for estimating the calorific value of coal based on a quantitative analysis of the elements in coal. Laser-induced breakdown spectroscopy (LIBS) was applied for the elemental analysis. Three different algorithms, including the correlation analysis (CA) method, the partial least squares (PLS) analysis method, and the signal-to-noise ratio (SNR), were adopted to accurately determine the concentrations of the elements in coal by using Dulong’s equation. Special emphasis was placed on the selection of the delay time to improve the measurement accuracy. The coefficient of determination, R2, was considered for optimizing the delay time. The intensity–concentration calibration curves were obtained for the elements in coal and the elemental concentration correlations were estimated on the basis of the calibration curves of each element. The CA showed a higher accuracy compared to PLS and the SNR. This confirmed that LIBS shows potential for the rapid determination of the calorific value of coal.