Hybrid classification of coal and biomass by laser-induced breakdown spectroscopy combined with K-means and SVM

Abstract

Laser-induced breakdown spectroscopy (LIBS) is a new technology suitable for classification of various materials. This paper proposes a hybrid classification scheme for coal, municipal sludge and biomass by using LIBS combined with K-means and support vector machine (SVM) algorithm. In the study, 10 samples were classified in 3 groups without supervision by K-means clustering, then a further supervised classification of 6 kinds of biomass samples by SVM was carried out. The results show that the comprehensive accuracy of the hybrid classification model is over 98%. In comparison with the single SVM classification model, the hybrid classification model can save 58.92% of operation time while guaranteeing the accuracy. The results demonstrate that the hybrid classification model is able to make an efficient, fast and accurate classification of coal, municipal sludge and biomass, furthermore, it is precise for the detection of various kinds of biomass fuel.