Rapid classification of coal by laser-induced breakdown spectroscopy (LIBS) with K-nearest neighbor (KNN) chemometrics

Abstract

It is important to classify coal in the industry to improve its utilization. Herein, coal classification was performed using laser-induced breakdown spectroscopy (LIBS) combined with K-nearest neighbor (KNN) chemometrics. The principal component analysis was used to determine the optimum component of the original data. Eight elements (Al, Fe, Ca, Na, Mg, Si, Ti, and K) were selected as the indices for coal classification, while 11 elements were further divided into four categories as indicators for coal classification using the KNN model. The standard coal samples were divided based upon the ash and volatile values and the elemental content. The results were satisfactory, achieving an optimum accuracy of 97.73%. In contrast to traditional methods, LIBS significantly reduced the analysis time, simplified the process, and maintained high accuracy.