Predicting ash content and water content in coal using full infrared spectra and machine learning models

Abstract

The aim of this study was to predict ash and water contents in coal samples using machine learning regression techniques, specifically LassoCV, RidgeCV, ElasticNetCV and LassoLarsCV. The analysis focused on finding non-zero coefficients at specific wavenumbers and highlighted the influence of infrared (IR) intensity on prediction accuracy. These determined wavenumbers were correlated with experimental ash and water contents in coal samples. The study showed a strong relationship between spectral features and regression coefficients, thus enabling accurate prediction of ash and water contents. For ash content, significant spectral features were identified at around 600 cm⁻¹ and 1600 cm⁻¹, corresponding to C=C and aromatic carbon vibrations. The prediction of water content was significantly influenced by O-H vibration at around 3700 cm⁻¹. The performance of the regression models was evaluated by comparing the predicted ash and water contents with experimental data, thus ensuring a strong correlation between the predicted and experimental values. This study highlighted the effectiveness of regression analysis and machine learning models in predicting coal properties and provided valuable information for better assessment of direct coal parameters.