Application of a Spectrum Standardization Method for Carbon Analysis in Coal Using Laser-Induced Breakdown Spectroscopy (LIBS). Issue 9 (September 2014)

• Record Type: Journal Article
• Authors: Li, Xiongwei; Wang, Zhe; Fu, Yangting; Li, Zheng; Liu, Jianmin; Ni, Weidou
• Is Part Of: Applied spectroscopy. Volume 68:Issue 9(2014)
• Page Start: 955
• Page End: 962
• Publication Date: 2014-09
• Subjects: Laser-induced breakdown spectroscopy -- LIBS -- Bituminous coal -- Standardization -- Quantitative measurement -- Precision; Spectrum analysis -- Periodicals; 543.505
• DOI: 10.1366/13-07345
• Languages: English
• ISSNs: 0003-7028

Abstract

Measurement of coal carbon content using laser-induced breakdown spectroscopy (LIBS) is limited by its low precision and accuracy. A modified spectrum standardization method was proposed to achieve both reproducible and accurate results for the quantitative analysis of carbon content in coal using LIBS. The proposed method used the molecular emissions of diatomic carbon (C2 ) and cyanide (CN) to compensate for the diminution of atomic carbon emissions in high volatile content coal samples caused by matrix effect. The compensated carbon line intensities were further converted into an assumed standard state with standard plasma temperature, electron number density, and total number density of carbon, under which the carbon line intensity is proportional to its concentration in the coal samples. To obtain better compensation for fluctuations of total carbon number density, the segmental spectral area was used and an iterative algorithm was applied that is different from our previous spectrum standardization calculations. The modified spectrum standardization model was applied to the measurement of carbon content in 24 bituminous coal samples. The results demonstrate that the proposed method has superior performance over the generally applied normalization methods. The average relative standard deviation was 3.21%, the coefficient of determination was 0.90, the root mean square error of prediction was 2.24%, and the average maximum relative error for the modified model was 12.18%, showing an overall improvement over the corresponding values for the normalization with segmental spectrum area, 6.00%, 0.75, 3.77%, and 15.40%, respectively.