Chinese Journal of Quantum Electronics, Volume. 41, Issue 3, 533(2024)

Online measurement of iron grade in iron concentrate slurry by LIBS based on SPA‐SVR model

ZHANG Qi1...2,3,4,5,*, ZHANG Zhansheng1, CHEN Tong2,3,4,5,6, ZHANG Peng2,3,4,5, QI Lifeng2,3,4,5, and SUN Lanxiang2,3,4,56 |Show fewer author(s)
Author Affiliations
  • 1Shenyang University of Chemical Technology, Shenyang 110142, China
  • 2State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
  • 3Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China
  • 4Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
  • 5Liaoning Liaohe Laboratory, Shenyang 110169, China
  • 6University of Chinese Academy of Sciences, Beijing 100049, China
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    Flotation is an important step in the ore dressing process, and the slurry grade during the flotation process is an important indicator that needs to be grasped in real-time in the ore dressing process. The authors' laboratory has developed an online slurry composition analyzer based on laser-induced breakdown spectroscopy(LIBS), SIA-LIBSlurry, which can measure the content of each element in the slurry during the flotation process in real time by collecting spectral data. However, the spectral data of iron ore slurry are of high dimensionality, and the strong multiple covariance and nonlinearity between the data increase the complexity of modeling. To address this issue, two variable selection algorithms are compared: the competitive adaptive reweighting algorithm (CARS) and the successive projection algorithm (SPA), and then the two algorithms with support vector machine regression (SVR) are combined to establish a quantitative analysis model. The results show that the SVR model built with the full spectrum of 6116 variables has low accuracy, with a root mean square error of prediction of 1.45%; the CARS-SVR model built with the 231 variables screened by CARS had improved predictive ability, with a root mean square error of prediction of 1.09%; and the best prediction is achieved by the SPA-SVR, a model built with the 12 variables screened by SPA, with a root mean square error of prediction down to 0.97%. Therefore, it is indicated that the SPA-SVR model has a high prediction accuracy, which helps to improve the accuracy of online monitoring of the SIA-LIBSlurry analyzer.

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    Qi ZHANG, Zhansheng ZHANG, Tong CHEN, Peng ZHANG, Lifeng QI, Lanxiang SUN. Online measurement of iron grade in iron concentrate slurry by LIBS based on SPA‐SVR model[J]. Chinese Journal of Quantum Electronics, 2024, 41(3): 533

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    Paper Information

    Category: Special Issue on Key Technologies and Applications of LIBS

    Received: Aug. 23, 2023

    Accepted: --

    Published Online: Jul. 17, 2024

    The Author Email: ZHANG Qi (zhangqi@sia.cn)

    DOI:10.3969/j.issn.1007-5461.2024.03.015

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