|
|
|
|
|
|
Quantitative Analysis of Mn and Ni Elements in Steel Based on LIBS and GA-PLS |
YANG Lin-yu1, 2, 3, DING Yu1, 2, 3*, ZHAN Ye4, ZHU Shao-nong1, 2, 3, CHEN Yu-juan1, 2, 3, DENG Fan1, 2, 3, ZHAO Xing-qiang1, 2, 3 |
1. Jiangsu Key Laboratory of Big Data Analysis Technology, Nanjing University of Information Science & Technology, Nanjing
210044, China
2. Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing 210044, China
3. Jiangsu Engineering Research Center on Meteorological Energy Using and Control, Nanjing University of Information Science & Technology, Nanjing 210044, China
4. College of Aviation Combat & Service, Aviation University of Air Force, Changchun 130022, China
|
|
|
Abstract The content of manganese and nickel in the steel refining process will affect the hardness and brittleness of the final product, but the added content needs to be strictly controlled. At the same time, the traditional steel composition detection equipment had a high cost, low efficiency and slow speed. Therefore, a high-precision, fast and real-time analysis method is needed. This article used genetic partial least squares (GA-PLS) combined with LIBS technology to quantitatively detect the two elements of Mn and Ni in the spectrum of steel samples and compared the results with the quantitative analysisof traditional PLS to verify the predictive performance of the GA-PLS model. This experiment used 12 steel samples purchased in the steel market, the spectral information of 9 samples was used as the calibration set training model, and the spectral information of 3 samples was used as the test set to verify the quantitative performance. GA-PLS continuously raised the threshold of the selected frequency of the variable, established the PLS model with the variables under different thresholds, and compared the threshold when the lowest RMSECV was selected (the optimal thresholds for the selected frequency of the spectral input variables of Mn and Ni were 8 and 7 respectively). The results of GA-PLS showed that the R2P and RMSEP of the GA-PLS manganese prediction results were 0.999 0 and 1.347 3, and the relative analysis error (RPD) was 2.5; the R2P and RMSEP of the nickel prediction results were 0.999 5 and 0.525 4, respectively, and the RPD was 8.6. The final predicted result was better than PLS. The results show that the GA-PLS algorithm has the potential for sustainable mining in metallurgical metal element analysis, and will also promote the deeper application of LIBS technology in the field of steel smelting.
|
Received: 2021-05-26
Accepted: 2021-07-28
|
|
Corresponding Authors:
DING Yu
E-mail: dingyuaoi@163.com
|
|
[1] Stehrer T, Praher B, Viskup R, et al. Journal of Analytical Atomic Spectrometry, 2009, 24(7): 973.
[2] Herrera K, Tognoni E, et al. Journal of Analytical Atomic Spectrometry, 2009, 24(4): 413.
[3] Singh J, Kumar R, Awasthi S, et al. Food Chemistry, 2017, 221: 1778.
[4] Kasem M A, Gonzalez J J, Russo R E, et al. Talanta, 2013, 108: 53.
[5] Deng F, Ding Y, et al. Plasma Science and Technology, 2020, 22(7): 74005.
[6] Liang J, Li M, et al. Chemometrics and Intelligent Laboratory Systems, 2020, 207: 104179.
[7] Gómez-Nubla L, Aramendia J, et al. Microchemical Journal, 2018, 137: 392.
[8] Li X W, Yang Y, et al. Plasma Science and Technology, 2020, 22(7): 122.
[9] Ding Y, Zhang W, et al. Journal of Analytical Atomic Spectrometry, 2020, 35(6): 1131.
[10] Li H, Huang M, et al. Optics Express, 2020, 28(2): 2142.
[11] ZHU Shao-nong, DING Yu, et al(朱绍农,丁 宇,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2020, 40(12): 3812.
[12] SHI Ming-xin, WANG Ao-song, et al(时铭鑫,王傲松,等). Metal. Anal.(冶金分析), 2021, 41(1): 30.
[13] GUO Lian-bo, ZHANG Yong, et al(郭连波,张 庸,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2014, 34(1): 217.
[14] DING Yu, XIONG Xiong, et al(丁 宇,熊 雄,等). Acta Photonica Sinica(光子学报), 2018, 47(8): 0847011.
[15] Shunsuke Kashiwakura, Kazuaki Wagatsuma. ISIJ International, 2020, 60(6): 1245.
[16] Sturm V, Erben B, Fleige R, et al. Optics Express, 2019, 27(25): 36855.
[17] Cui M, Deguchi Y, Wang Z, et al. Plasma Science and Technology, 2019, 21(3): 56.
[18] Shourian M, Mousavi S J. Water Resources Management, 2017, 31(15): 4835.
|
[1] |
ZHANG Xing-long1, LIU Yu-zhu1, 2*, SUN Zhong-mou1, ZHANG Qi-hang1, CHEN Yu1, MAYALIYA·Abulimiti3*. Online Monitoring of Pesticides Based on Laser Induced Breakdown
Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1711-1715. |
[2] |
HE Ya-xiong1, 2, ZHOU Wen-qi1, 2, ZHUANG Bin1, 2, ZHANG Yong-sheng1, 2, KE Chuan3, XU Tao1, 2*, ZHAO Yong1, 2, 3. Study on Time-Resolved Characteristics of Laser-Induced Argon Plasma[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1049-1057. |
[3] |
SUN Xue-hui1, ZHAO Bing2, LUO Zhen2, SUN Pei-jian1, PENG Bin1, NIE Cong1*, SHAO Xue-guang3*. Design and Application of the Discrimination Filter for Near-Infrared Spectroscopic Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(02): 399-404. |
[4] |
LI Ming-liang1, DAI Yu-jia1, QIN Shuang1, SONG Chao2*, GAO Xun1*, LIN Jing-quan1. Influence of LIBS Analysis Model on Quantitative Analysis Precision of Aluminum Alloy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(02): 587-591. |
[5] |
GONG Zheng1, LIN Jing-jun2*, LIN Xiao-mei3*, HUANG Yu-tao1. Effect of Heating and Cooling on the Characteristic Lines of Al During Melting[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(02): 598-602. |
[6] |
QIN Shuang1, LI Ming-liang1, DAI Yu-jia1, GAO Xun1*, SONG Chao2*, LIN Jing-quan1. The Accuracy Improvement of Fe Element in Aluminum Alloy by Millisecond Laser Induced Breakdown Spectroscopy Under Spatial Confinement Combined With Support Vector Machine[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(02): 582-586. |
[7] |
WANG Ya-wen1,2,3, ZHANG Yong4, CHEN Xiong-fei1,2,3, LIU Ying1,2,3, ZHAO Zhen-yang4, YE Ming-guo5, XU Yu-xing6, LIU Peng-yu1,2,3*. Quantitative Analysis of Nickel-Based Superalloys Based on a Remote LIBS System[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(02): 603-608. |
[8] |
CHEN Tao1, GUO Hui1, YUAN Man1, TAN Fu-yuan3*, LI Yi-zhou2*, LI Meng-long1. Recognition of Different Parts of Wild Cordyceps Sinensis Based on Infrared Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(12): 3727-3732. |
[9] |
QIU Meng-qing1, 2, XU Qing-shan1*, ZHENG Shou-guo1*, WENG Shi-zhuang3. Research Progress of Surface-Enhanced Raman Spectroscopy in Pesticide Residue Detection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(11): 3339-3346. |
[10] |
XU Yu-ting1, SUN Hao-ran2, GAO Xun1*, GUO Kai-min3*, LIN Jing-quan1. Identification of Pork Parts Based on LIBS Technology Combined With PCA-SVM Machine Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(11): 3572-3576. |
[11] |
DENG Fan1, HU Zhen-lin2, CUI Hao-hao2, ZHANG Deng2, TANG Yun4, ZHAO Zhi-fang2, ZENG Qing-dong2, 3*, GUO Lian-bo2*. Progress in the Correction of Self-Absorption Effect on Laser-Induced Breakdown Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(10): 2989-2998. |
[12] |
WANG Guo-liang1, 2, YU Ke-qiang3, CHENG Kai2, LIU Xin2, WANG Wen-jun1, LI Hong2, GUO Er-hu2, LI Zhi-wei1*. Hyperspectral Technique Coupled With Chemometrics Methods for Predicting Alkali Spreading Value of Millet Flour[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(10): 3189-3193. |
[13] |
YOU Wen1, XIA Yang-peng1, HUANG Yu-tao1, LIN Jing-jun2*, LIN Xiao-mei3*. Research on Selection Method of LIBS Feature Variables Based on CART Regression Tree[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(10): 3240-3244. |
[14] |
YANG Wen-feng1*, QIAN Zi-ran1, CAO Yu2, WEI Gui-ming1, ZHU De-hua2, WANG Feng3, FU Chan-yuan1. Research on the Controllability of Aircraft Skin Laser Paint Remove Based on Laser-Induced Breakdown Spectrum and Composition Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(10): 3233-3239. |
[15] |
HE Ya-xiong1, ZHOU Wen-qi1, KE Chuan2, XU Tao1*, ZHAO Yong1, 2. Review of Laser-Induced Breakdown Spectroscopy in Gas Detection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(09): 2681-2687. |
|
|
|
|