Diffuse reflectance spectroscopy based rapid coal rank estimation: A machine learning enabled framework

Abstract

This research aims at studying the ability of using diffuse reflectance spectroscopy (DRS) for discriminating or classifying coal samples into different ranks. Spectral characteristics such as the shape of the spectral profile, slope, absorption intensity of coal samples of different ranks ranging from lignite A to semi-anthracite were studied in the Vis-NIR-SWIR (350–2500 nm) range. A number of classification algorithms (Logistic Regression, Random Forest, and SVM) were trained using the DRS dataset of coal samples. Class imbalances present in the dataset were handled using different approaches (SMOTE and Oversampling of minority classes), which improved the classification accuracy. Coal samples were initially classified into broad classes viz., lignite, sub-bituminous, bituminous, and anthracite with an accuracy of 0.98 and F1 score of 0.75. Later, the same samples were further classified into sub-class levels. The sub-class level classification also obtained good results with an accuracy of 0.77 and F1 score of 0.64. The results demonstrate the effectiveness of rapid coal classification systems based on DRS dataset in combination with different machine learning-based classification algorithms.