Accuracy improvement of quantitative analysis of calorific value of coal by combining support vector machine and partial least square methods in laser- induced breakdown spectroscopy

Xiongwei LI (李雄威); Yang YANG (杨阳); Gengda LI (李庚达); Baowei CHEN (陈保卫); Wensen HU (胡文森)

doi:10.1088/2058-6272/ab8972

Plasma Science and Technology > 2020 > 22(7): 74014-074014. > DOI: 10.1088/2058-6272/ab8972

Xiongwei LI (李雄威), Yang YANG (杨阳), Gengda LI (李庚达), Baowei CHEN (陈保卫), Wensen HU (胡文森). Accuracy improvement of quantitative analysis of calorific value of coal by combining support vector machine and partial least square methods in laser- induced breakdown spectroscopy[J]. Plasma Science and Technology, 2020, 22(7): 74014-074014. DOI: 10.1088/2058-6272/ab8972

Citation:

PDF (613 KB)

Accuracy improvement of quantitative analysis of calorific value of coal by combining support vector machine and partial least square methods in laser- induced breakdown spectroscopy

Guodian New Energy Technology Research Institute, Beijing 102209, People's Republic of China

Funds: The research was supported by the key R&D program of China Energy Investment Corporation (GJNY-18-27) and National Natural Science Foundation of China (Nos. 61675110 and 51906124).

More Information

Received Date: December 29, 2019
Revised Date: April 13, 2020
Accepted Date: April 14, 2020

Graphical Abstract

Abstract

Abstract

Laser-induced breakdown spectroscopy (LIBS) is a potential technology for online coal property analysis, but successful quantitative measurement of calorific value using LIBS suffers from relatively low accuracy caused by the matrix effect. To solve this problem, the support vector machine (SVM) and the partial least square (PLS) were combined to increase the measurement accuracy of calorific value in this study. The combination model utilized SVM to classify coal samples into two groups according to their volatile matter contents to reduce the matrix effect, and then applied PLS to establish calibration models for each sample group respectively. The proposed model was applied to the measurement of calorific values of 53 coal samples, showing that the proposed model could greatly increase accuracy of the measurement of calorific values. Compared with the traditional PLS method, the coefficient of determination (R ² ) was improved from 0.93 to 0.97, the root-mean-square error of prediction was reduced from 1.68 MJkg ⁻¹ to 1.08 MJkg ⁻¹, and the average relative error was decreased from 6.7% to 3.93%, showing an overall improvement.
- accuracy improvement,
- calorific value,
- coal,
- PLS,
- SVM,
- LIBS

FullText(HTML)

References (20)

References

[1]	Yuan T B et al 2013 J. Anal. At. Spectrom. 28 1045
[2]	Wang Z, Dong F Z and Zhou W D 2015 Plasma Sci. Technol.17 617
[3]	Cremers D A and Chinni R C 2009 Appl. Spectrosc. Rev. 44 457
[4]	Wang Z et al 2014 Front. Phys. 9 419
[5]	Fu Y T et al 2019 Plasma Sci. Technol. 21 030101
[6]	Wang Z Z et al 2016 Front. Phys. 11 114213
[7]	Dong M R et al 2019 Spectrosc. Spect. Anal. 39 2202 (in Chinese)
[8]	Hou Z Y et al 2016 J. Anal. At. Spectrom. 31 722
[9]	Zhang L et al 2015 Spectrochim. Acta Part B: Atomic Spectrosc. 113 167
[10]	Li W B et al 2018 Energy Fuels 32 24
[11]	Yu J et al 2012 Front. Phys. 7 649
[12]	Castle B C et al 1998 Appl. Spectrosc. 52 649
[13]	Ma Q L et al 2010 Spectrochim. Acta Part B: Atomic Spectrosc. 65 896
[14]	Li X W et al 2014 Appl. Spectrosc. 68 955
[15]	Li X W et al 2015 Plasma Sci. Technol. 17 621
[16]	Li X W et al 2014 Spectrochim. Acta Part B: Atomic Spectrosc. 99 82
[17]	Vapnik V 1998 Statistical Learning Theory (New York: Wiley)
[18]	Brereton R G 2000 Analyst 125 2125
[19]	Dong M R et al 2014 Spectrochim. Acta Part B: Atomic Spectrosc. 100 62
[20]	Feng J et al 2011 Anal. Bioanal. Chem. 400 3261

[1]	Luyun JIANG, Yutong CHEN, Chentao MAO, Jianhui HAN, Anmin CHEN, Jifei YE. Performance optimization of ammonium dinitramide-based liquid propellant in pulsed laser ablation micro-propulsion using LIBS[J]. Plasma Science and Technology, 2025, 27(1): 015503. DOI: 10.1088/2058-6272/ad92f8
[2]	Junwei JIA, Zhifeng LIU, Congyuan PAN, Huaqin XUE. Detection of Al, Mg, Ca, and Zn in copper slag by LIBS combined with calibration curve and PLSR methods[J]. Plasma Science and Technology, 2024, 26(2): 025507. DOI: 10.1088/2058-6272/ad1045
[3]	Jiajia HOU (侯佳佳), Lei ZHANG (张雷), Yang ZHAO (赵洋), Zhe WANG (王哲), Yong ZHANG (张勇), Weiguang MA (马维光), Lei DONG (董磊), Wangbao YIN (尹王保), Liantuan XIAO (肖连团), Suotang JIA (贾锁堂). Mechanisms and efficient elimination approaches of self-absorption in LIBS[J]. Plasma Science and Technology, 2019, 21(3): 34016-034016. DOI: 10.1088/2058-6272/aaf875
[4]	Xiaomeng LI (李晓萌), Huili LU (陆慧丽), Jianhong YANG (阳建宏), Fu CHANG (常福). Semi-supervised LIBS quantitative analysis method based on co-training regression model with selection of effective unlabeled samples[J]. Plasma Science and Technology, 2019, 21(3): 34015-034015. DOI: 10.1088/2058-6272/aaee14
[5]	Haobin PENG (彭浩斌), Guohua CHEN (陈国华), Xiaoxuan CHEN (陈小玄), Zhimin LU (卢志民), Shunchun YAO (姚顺春). Hybrid classification of coal and biomass by laser-induced breakdown spectroscopy combined with K-means and SVM[J]. Plasma Science and Technology, 2019, 21(3): 34008-034008. DOI: 10.1088/2058-6272/aaebc4
[6]	Shuxia ZHAO (赵书霞), Lei ZHANG (张雷), Jiajia HOU (侯佳佳), Yang ZHAO (赵洋), Wangbao YIN (尹王保), Weiguang MA (马维光), Lei DONG (董磊), Liantuan XIAO (肖连团), Suotang JIA (贾锁堂). Accurate quantitative CF-LIBS analysis of both major and minor elements in alloys via iterative correction of plasma temperature and spectral intensity[J]. Plasma Science and Technology, 2018, 20(3): 35502-035502. DOI: 10.1088/2058-6272/aa97ce
[7]	F. MEHARI, M. ROHDE, C. KNIPFER, R. KANAWADE, F. KL¨AMPFL, W. ADLER, N. OETTER, F. STELZLE, M. SCHMIDT. Investigation of Laser Induced Breakdown Spectroscopy (LIBS) for the Differentiation of Nerve and Gland Tissue–A Possible Application for a Laser Surgery Feedback Control Mechanism[J]. Plasma Science and Technology, 2016, 18(6): 654-660. DOI: 10.1088/1009-0630/18/6/12
[8]	LI Xiongwei (李雄威), MAO Xianglei (毛向雷), WANG Zhe (王哲), Richard E. RUSSO. Quantitative Analysis of Carbon Content in Bituminous Coal by Laser-Induced Breakdown Spectroscopy Using UV Laser Radiation[J]. Plasma Science and Technology, 2015, 17(11): 928-932. DOI: 10.1088/1009-0630/17/11/07
[9]	YANG Guang (杨光), QIAO Shujun (乔淑君), CHEN Pengfei (陈鹏飞), DING Yu (丁宇), TIAN Di (田地). Rock and Soil Classification Using PLS-DA and SVM Combined with a Laser-Induced Breakdown Spectroscopy Library[J]. Plasma Science and Technology, 2015, 17(8): 656-663. DOI: 10.1088/1009-0630/17/8/08
[10]	WEN Guanhong(温冠宏), SUN Duixiong(孙对兄), SU Maogen(苏茂根), DONG Chenzhong(董晨钟). LIBS Detection of Heavy Metal Elements in Liquid Solutions by Using Wood Pellet as Sample Matrix[J]. Plasma Science and Technology, 2014, 16(6): 598-601. DOI: 10.1088/1009-0630/16/6/11

Cited By

Cited by

Periodical cited type(14)

1.	Zheng, Z.-H., Shi, Y., Du, J. et al. Research on the calorific value detection method and influencing mechanism of solid materials via EDXRF. Spectrochimica Acta - Part B Atomic Spectroscopy, 2025. DOI:10.1016/j.sab.2025.107154
2.	Dong, M., Cai, J., Liu, H. et al. A review of laser-induced breakdown spectroscopy and spontaneous emission techniques in monitoring thermal conversion of fuels. Spectrochimica Acta - Part B Atomic Spectroscopy, 2023. DOI:10.1016/j.sab.2023.106807
3.	Zhang, C., Song, W., Hou, Z. et al. Improving quantitative analysis of cement elements in laser-induced breakdown spectroscopy through combining matrix matching with regression. Journal of Analytical Atomic Spectrometry, 2023, 38(12): 2554-2561. DOI:10.1039/d3ja00306j
4.	Yao, S., Yu, Z., Xu, S. et al. Investigation on Laser-induced Plasma Characterization of Coal Under Argon Atmosphere \| [氩气环境下煤炭的激光诱导等离子体特性研究]. Kung Cheng Je Wu Li Hsueh Pao/Journal of Engineering Thermophysics, 2023, 44(11): 3140-3150.
5.	Dou, Y., Wang, Q., Wang, S. et al. Quantitative Analysis of Coal Quality by a Portable Laser Induced Breakdown Spectroscopy and Three Chemometrics Methods. Applied Sciences (Switzerland), 2023, 13(18): 10049. DOI:10.3390/app131810049
6.	Long, J., Song, W., Hou, Z. et al. A data selection method for matrix effects and uncertainty reduction for laser-induced breakdown spectroscopy. Plasma Science and Technology, 2023, 25(7): 075501. DOI:10.1088/2058-6272/acb6dd
7.	Guan, C., Wu, T., Chen, J. et al. Detection of Carbon Content from Pulverized Coal Using LIBS Coupled with DSC-PLS Method. Chemosensors, 2022, 10(11): 490. DOI:10.3390/chemosensors10110490
8.	Huang, Q., Jiang, Z., Xie, Y. et al. On-line detection method of silicon mass fraction in pig iron based on LIBS spectral line fitting optimization \| [基于LIBS谱线拟合优化的生铁硅质量分数在线检测方法]. Zhongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Central South University (Science and Technology), 2022, 53(4): 1167-1178. DOI:10.11817/j.issn.1672-7207.2022.04.002
9.	Zhang, D., Zhang, H., Zhao, Y. et al. A brief review of new data analysis methods of laser-induced breakdown spectroscopy: machine learning. Applied Spectroscopy Reviews, 2022, 57(2): 89-111. DOI:10.1080/05704928.2020.1843175
10.	Liu, K., He, C., Zhu, C. et al. A review of laser-induced breakdown spectroscopy for coal analysis. TrAC - Trends in Analytical Chemistry, 2021. DOI:10.1016/j.trac.2021.116357
11.	Chen, J., Zhan, K., Li, Q. et al. Spectral clustering based on histogram of oriented gradient (HOG) of coal using laser-induced breakdown spectroscopy. Journal of Analytical Atomic Spectrometry, 2021, 36(6): 1297-1305. DOI:10.1039/d1ja00104c
12.	Wang, Y., Li, R., Chen, Y. Accurate elemental analysis of alloy samples with high repetition rate laser-ablation spark-induced breakdown spectroscopy coupled with particle swarm optimization-extreme learning machine. Spectrochimica Acta - Part B Atomic Spectroscopy, 2021. DOI:10.1016/j.sab.2021.106077
13.	Chen, G., Wang, Q., Teng, G. et al. Influence of polarization of laser beam on emission intensity of nanosecond laser-induced breakdown spectroscopy. Proceedings of SPIE - The International Society for Optical Engineering, 2021. DOI:10.1117/12.2607071
14.	Liu, J., Dou, Y., Zuo, G. Design of sea ice monitoring UAV platform based on machine learning. Journal of Physics: Conference Series, 2020, 1654(1): 12067. DOI:10.1088/1742-6596/1654/1/012067

Other cited types(0)

Get Citation

PDF

XML

Article views (148) PDF downloads (60) Cited by(14)

Turn off MathJax

Article Contents

Abstract

References

Accuracy improvement of quantitative analysis of calorific value of coal by combining support vector machine and partial least square methods in laser- induced breakdown spectroscopy