Quantitative analysis of carbon in coal based on laser-induced breakdown spectroscopy

HAO Xiaojian; REN Long; YANG Yanwei; SUN Yongkai

doi:10.7510/jgjs.issn.1001-3806.2020.02.017

LASER TECHNOLOGY > 2020 > 44(2): 232-236. > DOI: 10.7510/jgjs.issn.1001-3806.2020.02.017

HAO Xiaojian, REN Long, YANG Yanwei, SUN Yongkai. Quantitative analysis of carbon in coal based on laser-induced breakdown spectroscopy[J]. LASER TECHNOLOGY, 2020, 44(2): 232-236. DOI: 10.7510/jgjs.issn.1001-3806.2020.02.017

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Quantitative analysis of carbon in coal based on laser-induced breakdown spectroscopy

Key Laboratory of Instrumentation Science & Dynamic Measurement of Ministry of Education, North University of China, Taiyuan 030051, China

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Received Date: April 09, 2019
Revised Date: May 15, 2019
Published Date: March 24, 2020

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Abstract

Abstract

In quantitative analysis of carbon content in coal samples, matrix effect has great influence and prediction accuracy is low. In order to solve this problem, under the optimum experimental conditions, spectral data of 14 standard coal samples after laser-induced breakdown spectroscopy (LIBS) test were obtained. C Ⅰ 193.09nm wavelength with good independence and without interference from adjacent spectral lines was selected. Integral strength was taken as input variable. Basic curve calibration method and neural network calibration method were used to carry out quantitative analysis of coal samples. The results show that, when basic calibration curve method is used, it is greatly affected by noise interference and matrix effect. Average relative error is 15.39%. When neural network calibration method is used, relative error of the validated samples decreases by 7.54% on average. Neural network calibration method can effectively reduce the quantitative analysis error and improve the ability of LIBS to predict carbon content in coal. This study can provide guidance for quantitative analysis of carbon content in coal.
- spectroscopy,
- quantitative analysis,
- neural network,
- coal

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