Quantitative Analysis of Coal Quality Using Laser-Induced Breakdown Spectroscopy

The study presents a novel approach that integrates laser-induced breakdown spec-troscopy (LIBS) data with machine learning algorithms for the rapid evaluation of coal quality. The developed framework enables quantitative determination of three critical parameters: Ash Content (Aad), Carbon Content (Cd), Sulfur Content (Stad). The experimental implementation utilized an optimized dataset to construct and evaluate the predictive model. The LIBS prototype system enables spectral data acqui-sition under controlled experimental conditions. Data preprocessing is carried out by systematically removing background interference, substrate effects, and saturated signals using adaptive filtering techniques. Characteristic emission peaks correspond-ing to target elements are identified through multivariate analysis, and Partial Least Squares Regression (PLSR) serves as the core algorithm for quantitative analysis. Sys-tematic iterative optimization of multivariate preprocessing parameters and adaptive peak selection strategies yields substantial improvements in both predictive accuracy and computational efficiency, with determination coefficients (R² > 0.90) demonstrat-ed for all target analytes. This enhanced accuracy validates the viability of LIBS as a robust alternative to conventional analytical methods for coal composition analysis. The LIBS demonstrates substantial advantages in coal quality assessment, thereby en-hancing the overall efficiency of both coal extraction and quality evaluation processes.