Determination of ash content, volatile matter, and calorific value in coal by OLS combined with laser-induced breakdown spectroscopy based on PC recombination

Abstract

Coal quality analysis is important to promote the rational and sustainable utilization of coal resources. The traditional method of selecting characteristic lines from the coal sample spectrum is time-consuming. This work aims to use laser-induced breakdown spectroscopy (LIBS) to reconstruct principal components (PCs) by utilizing the correlation between full spectrum wave point interpretation variance and calibration values. More components related to the features of interest are obtained and the regression metrics are maximized. Subsequently, quantitative models were established using ordinary least squares (OLS) for ash content, volatile matter, and calorific value. The coefficient of determination (R²) of the training set and test set of ash content and volatile matter was improved from 0.8125 and 0.8222, 0.6502 and 0.6483 to 0.9701 and 0.9818, 0.9458 and 0.9429, respectively. The root-mean-square error of cross-validation (RMSECV) and the root-mean-square error of prediction (RMSEP) were reduced from 1.7843% and 1.7696%, 1.1797% and 1.4301% to 0.7153% and 0.7037%, 0.5678% and 0.6628%, respectively. The training set and test set of calorific value R² were also improved from 0.7086 and 0.7157 to 0.9857 and 0.9811. RMSECV and RMSEP were reduced from 0.9525 MJ kg⁻¹ and 1.0178 MJ kg⁻¹ to 0.1518 MJ kg⁻¹ and 0.1613 MJ kg⁻¹, respectively. To evaluate the interpretability of PC recombination, the cumulative correlations between ash content, volatile matter, calorific value and the PCs of each wave point in the LIBS spectrum were analyzed, and then the characteristic spectral line locations of the elements that contributed the most to the calibration model were extracted. The results of the analysis of metal and non-metal elements in coal samples show that PC recombination chooses more significant characteristics rather than noise as independent variables. The work demonstrates the feasibility and effectiveness of employing LIBS-based quantitative analysis for rapid coal quality detection in the coal industry.