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

论文摘要

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.

论文目录

文章来源

类型: 期刊论文

作者: 彭浩斌,陈国华,陈小玄,卢志民,姚顺春

来源: Plasma Science and Technology 2019年03期

年度: 2019

分类: 基础科学,工程科技Ⅰ辑,工程科技Ⅱ辑

专业: 化学,新能源

单位: Institute of Safety Science & Engineering, South China University of Technology,School of Electric Power, South China University of Technology

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)

分类号: O657.31;TK6

页码: 64-72

总页数: 9

文件大小: 1443K

下载量: 55

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