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Metal Mine ›› 2018, Vol. 47 ›› Issue (12): 140-145.

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In-situ Determination Method of BIF Iron Ore Based on Visible and Near-Infrared Spectrum

He Qun1,Wang Dong1,Liu Shanjun1,Mao Yachun1,Sun Houguang2,Sun Mingchen2   

  1. 1. School of Resources and Civil Engineering, Northeastern University, Shenyang 110819,China;2. Anqian Mining Company,Anshan Iron & Steel Group,Anshan 114043,China
  • Online:2018-12-15 Published:2019-01-17

Abstract: Banded iron formation (BIF) is the main type of iron ore resources in China.The main way of mining is open-pit mining.At present,the determination method of grade is mainly based on traditional laboratory methods,tt has the disadvantages of low sampling density,long testing period and low efficiency,moreover,there is a lag effect in the grade test results,which leads to boundary delineation of orebodies is inaccurate,and the increasing of ore loss rate and dilution rate,beside that,the quality of ore matching is affected seriously.Taking Anqian Open-pit Mine as the test site,through field sample,spectral testing,model establishment,model validation test processes to reveal spectral characteristics of different rock types.The relationship between grade and spectral characteristics is established,and the method for rapid in-situ determination of BIF iron ore is studied.The results show that there are significant spectral differences among hematite,magnetite and surrounding rocks,the rock and iron ore type identification and classification model based on these differences is established,its distinction accuracy between iron ore and surrounding rock is 97.1% and the distinction accuracy between hematite and magnetite is 93.5%;the grade regression model established by partial least squares method (PLS) has a precision of 3.54%.The above study results laid a foundation for the accurate mining and rational ore matching of open-pit iron mine.

Key words: BIF iron, Spectral Characteristics, Classification and recognition, Quantitative inversion, In-situ determination, Open-pit mining, Partial least squares method