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

作     者：Haobin PENG Guohua CHEN Xiaoxuan CHEN Zhimin LU Shunchun YAO 彭浩斌;陈国华;陈小玄;卢志民;姚顺春

作者机构：Institute of Safety Science & Engineering South China University of Technology School of Electric Power South China University of Technology Guangdong Province Engineering Research Center of High Efficient and Low Pollution Energy Conversion 

出 版 物：《Plasma Science and Technology》 (等离子体科学和技术（英文版）)

年 卷 期：2019年第21卷第3期

页      面：60-68页

核心收录：

学科分类：08[工学] 0827[工学-核科学与技术] 

基　　金：supported by National Natural Science Foundation of China (No. 51 676 073) the Guangdong Province Train High-Level Personnel Special Support Program (No. 2014TQ01N334) the Science and Technology Project of Guangdong Province (No. 2015A020215005) the Guangdong Province Key Laboratory of Efficient and Clean Energy Utilization (No. 2013A061401005) 

主　　题：laser-induced breakdown spectroscopy hybrid classification model biomass K-means support vector machine 

摘      要：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.