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The basicity analysis of sintered ore using laser-induced breakdown spectroscopy (LIBS) combined with random forests regression(RFR)

Abstract

The basicity of sintered ore, which is related to the melting point of the sinter, is vital to ore mining and blast furnace smelting. Laser-induced breakdown spectroscopy (LIBS) with random forests regression (RFR) has been applied to measuring the basicity of sintered ore, which can be defined by the concentrations of oxides: CaO, SiO2, Al2O3 and MgO. In this work, thirty sintered ore samples are used, of which twenty samples are used for calibration set to construct the random forests regression (RFR) calibration model for the above-mentioned oxides and ten samples are used for test set. The characteristic line of main components in the sintered ore are identified by national institute of standards and technology(NIST) database. Two model parameters (the number of decision trees–ntree and the number of random variables--mtry) of RFR was optimized by out-of-bag (OOB) error estimation for improving the predictive accuracy of the RFR model. The RFR model was applied to sample measurements and the results were compared with partial least square regression (PLSR) models which has shown better predictive capabilities than the PLSR model. In order to verify the stability of RFR model, fifty measurements were made and relative standard deviation(RSD) of the data between 0.27% and 0.59%. Therefore, LIBS combined with RFR could be a promising method for real-time online, rapid analysis in mining and mineral processing industries.

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Publication details

The article was received on 05 Jun 2017, accepted on 01 Aug 2017 and first published on 02 Aug 2017


Article type: Paper
DOI: 10.1039/C7AY01389B
Citation: Anal. Methods, 2017, Accepted Manuscript
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    The basicity analysis of sintered ore using laser-induced breakdown spectroscopy (LIBS) combined with random forests regression(RFR)

    G. Yang, X. Han, C. Wang, Y. Ding, K. Liu, D. Tian and L. Yao, Anal. Methods, 2017, Accepted Manuscript , DOI: 10.1039/C7AY01389B

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