Detection of Al, Mg, Ca, and Zn in copper slag by LIBS combined with calibration curve and PLSR methods

Junwei JIA; Zhifeng LIU; Congyuan PAN; Huaqin XUE

doi:10.1088/2058-6272/ad1045

Plasma Science and Technology > 2024 > 26(2): 025507. > DOI: 10.1088/2058-6272/ad1045

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

Citation:

PDF (2725 KB)

Detection of Al, Mg, Ca, and Zn in copper slag by LIBS combined with calibration curve and PLSR methods

1.
School of Mechanical Engineering, Hefei University of Technology, Hefei 230009, People’s Republic of China
2.
Hefei GStar Intelligent Control Technical Co. Ltd., Hefei 230088, People’s Republic of China
3.
Anhui Industrial Combustion Monitoring Technology and Equipment Engineering Laboratory, Hefei 230088, People’s Republic of China

More Information

Author Bio:
Junwei JIA: jjw2014@mail.ustc.edu.cn
Corresponding author:
Junwei JIA, jjw2014@mail.ustc.edu.cn
Received Date: June 05, 2023
Revised Date: November 23, 2023
Accepted Date: November 27, 2023
Available Online: January 15, 2024
Published Date: February 04, 2024

Graphical Abstract

Abstract

Abstract

The precise measurement of Al, Mg, Ca, and Zn composition in copper slag is crucial for effective process control of copper pyrometallurgy. In this study, a remote laser-induced breakdown spectroscopy (LIBS) system was utilized for the spectral analysis of copper slag samples at a distance of 2.5 m. The composition of copper slag was then analyzed using both the calibration curve (CC) method and the partial least squares regression (PLSR) analysis method based on the characteristic spectral intensity ratio. The performance of the two analysis methods was gauged through the determination coefficient (R²), average relative error (ARE), root mean square error of calibration (RMSEC), and root mean square error of prediction (RMSEP). The results demonstrate that the PLSR method significantly improved both R² for the calibration and test sets while reducing ARE, RMSEC, and RMSEP by 50% compared to the CC method. The results suggest that the combination of LIBS and PLSR is a viable approach for effectively detecting the elemental concentration in copper slag and holds potential for online detection of the elemental composition of high-temperature molten copper slag.
- copper slag,
- element,
- remote,
- LIBS,
- PLSR

FullText(HTML)

Acknowledgments

This work is supported by funding for research activities of postdoctoral researchers in Anhui Province and special funds for developing Anhui Province’s industrial “three highs” and high-tech industries.

References (26)

References

[1]	Lohmeier S et al 2021 J. S. Afr. Inst. Min. Metall. 121 129
[2]	Jiang L F et al 2022 J. Phys.: Conf. Ser. 2268 012006
[3]	Chen J et al 2022 JOM 74 185 doi: 10.1007/s11837-021-04920-7
[4]	Hahn D W and Omenetto N 2010 Appl. Spectrosc. 64 335A doi: 10.1366/000370210793561691
[5]	Hahn D W and Omenetto N 2012 Appl. Spectrosc. 66 347 doi: 10.1366/11-06574
[6]	Ma S X et al 2023 J. Hazard. Mater. 443 130188 doi: 10.1016/j.jhazmat.2022.130188
[7]	Elhamdaoui I et al 2022 J. Anal. At. Spectrom. 37 2537 doi: 10.1039/D2JA00120A
[8]	Bol'shakov A A et al 2021 Spectrochim. Acta Part B 179 106094 doi: 10.1016/j.sab.2021.106094
[9]	Aints M et al 2018 J. Spectrosc. 2018 4605925
[10]	Myakalwar A K et al 2021 Minerals 11 1073
[11]	Meng D S et al 2017 Spectrochim. Acta Part B 137 39 doi: 10.1016/j.sab.2017.09.011
[12]	Sun L X et al 2018 Spectrochim. Acta Part B 142 29 doi: 10.1016/j.sab.2018.02.005
[13]	Zeng G D et al 2022 Plasma Sci. Technol. 24 084009 doi: 10.1088/2058-6272/ac72e3
[14]	Petersson J, Gilbert-Gatty M and Bengtson A 2020 J. Anal. At. Spectrom. 35 1848 doi: 10.1039/D0JA00188K
[15]	Hai R et al 2021 Appl. Phys. B 127 37
[16]	Dai Y J et al 2021 J. Anal. At. Spectrom. 36 1634 doi: 10.1039/D1JA00082A
[17]	Cui M C et al 2022 Spectrochim. Acta Part B 191 106398 doi: 10.1016/j.sab.2022.106398
[18]	Ahmed N et al 2022 Anal. Lett. 55 2239 doi: 10.1080/00032719.2022.2052307
[19]	François E et al 2020 Spectrochim. Acta Part B 170 105921 doi: 10.1016/j.sab.2020.105921
[20]	Yoon S et al 2021 Appl. Sci. 11 7154 doi: 10.3390/app11157154
[21]	Kashiwakura S and Wagatsuma K 2020 ISIJ Int. 60 1245 doi: 10.2355/isijinternational.ISIJINT-2019-549
[22]	Brinkmann P et al 2023 Minerals 13 113
[23]	Pan C Y et al 2021 Metall. Anal. 41 41 (in Chinese)
[24]	Han Z Y et al 2015 Spectrosc. Spect. Anal. 35 304 (in Chinese)
[25]	Kramida A et al 2020 NIST atomic spectra database (version 5.8) Gaithersburg: National Institute of Standards and Technology
[26]	Sato T et al 2019 Plasma Sci. Technol. 21 034021 doi: 10.1088/2058-6272/aaf5ef

[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]	Yihan LYU, Weiran SONG, Zongyu HOU, Zhe WANG. Incorporating empirical knowledge into data-driven variable selection for quantitative analysis of coal ash content by laser-induced breakdown spectroscopy[J]. Plasma Science and Technology, 2024, 26(7): 075509. DOI: 10.1088/2058-6272/ad370c
[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]	Minchao CUI (崔敏超), Yoshihiro DEGUCHI (出口祥啓), Zhenzhen WANG (王珍珍), Seiya TANAKA (田中诚也), Min-Gyu JEON (全敏奎), Yuki FUJITA (藤田裕贵), Shengdun ZHAO (赵升吨). Remote open-path laser-induced breakdown spectroscopy for the analysis of manganese in steel samples at high temperature[J]. Plasma Science and Technology, 2019, 21(3): 34007-034007. DOI: 10.1088/2058-6272/aaeba7
[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]	Dongxu CHEN (陈东旭), Yilun ZHU (朱逸伦), Zhenling ZHAO (赵朕领), Chengming QU (渠承明), Wang LIAO (廖望), Jinlin XIE (谢锦林), Wandong LIU (刘万东). An intelligent remote control system for ECEI on EAST[J]. Plasma Science and Technology, 2017, 19(8): 84005-084005. DOI: 10.1088/2058-6272/aa6e4a
[8]	ZHONG Shilei (钟石磊), ZHENG Ronger (郑荣儿), LU Yuan (卢渊), CHENG Kai (程凯), XIU Junshan (修俊山). Ultrasonic Nebulizer Assisted LIBS: a Promising Metal Elements Detection Method for Aqueous Sample Analysis[J]. Plasma Science and Technology, 2015, 17(11): 979-984. DOI: 10.1088/1009-0630/17/11/17
[9]	LIU Xiaona (刘晓娜), HUANG Jianmei (黄建梅), WU Zhisheng (吴志生), ZHANG Qiao (张乔), SHI Xinyuan (史新元), ZHAO Na (赵娜), JIA Shuaiyun (贾帅芸), QIAO Yanjiang (乔延江). Microanalysis of Multi-Element in Juncus effusus L. by LIBS Technique[J]. Plasma Science and Technology, 2015, 17(11): 904-908. DOI: 10.1088/1009-0630/17/11/02
[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(5)

1.	Niu, Y., Bao, W., Liu, D. et al. Analysis of enthalpy and energy conversion efficiency in high-power inductively coupled plasma. Vacuum, 2024. DOI:10.1016/j.vacuum.2024.113220
2.	Zhou, X., Chen, X., Ye, T. et al. Quasi-direct numerical simulations of the flow characteristics of a thermal plasma reactor with counterflow jet. Plasma Science and Technology, 2023, 25(7): 075403. DOI:10.1088/2058-6272/acb9d8
3.	Niu, Y., Bao, W., Liu, D. et al. Thermodynamic Parameters and Energy Transfer Analysis of High Enthalpy Inductively Coupled Plasma. 2023. DOI:10.1109/CSRSWTC60855.2023.10427285
4.	Zhou, X., Chen, X., Ye, T. et al. Numerical study of the effect of coflow argon jet on a laminar argon thermal plasma jet. Plasma Science and Technology, 2022, 24(5): 055409. DOI:10.1088/2058-6272/ac52eb
5.	Bykov, N.Y., Obraztsov, N.V., Hvatov, A.A. et al. Hybrid modeling of gas-dynamic processes in AC plasma torches. Materials Physics and Mechanics, 2022, 50(2): 287-303. DOI:10.18149/MPM.5022022_9

Other cited types(0)

Figures(6) / Tables(4)

Get Citation

PDF

XML

Article views (36) PDF downloads (16) Cited by(5)

Turn off MathJax

Article Contents

Abstract

Acknowledgments

References

Detection of Al, Mg, Ca, and Zn in copper slag by LIBS combined with calibration curve and PLSR methods

Abstract