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A Method Based on Thermal Infrared Spectrum for Analysis of SiO2 Content in Anshan-Type Iron |
WANG Dong, LIU Shan-jun*, MAO Ya-chun, WANG Yue, LI Tian-zi |
School of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China |
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Abstract The SiO2 content of iron is an essential index to control and measure the quality of iron ore. It is important to determine the method of mineral processing and the process of ore blending. The traditional method of SiO2 content measurement has the defects of heavy workload, complex operations and long period,thus it is difficult to determine SiO2 content of iron quickly and efficiently. The thermal infrared spectrum data of Anshan-type iron experimental samples from Anqian mining area of Liaoning province was measured and collected by Turbo FT. The spectral characteristics of the experimental samples were analyzed. In addition, the RI, DI and NDI were established based on the spectrum data of the samples. The most sensitive waveband and correlation coefficient between spectral indexes and SiO2 content were determined. The NDI which had the most significant correlation with SiO2 content was selected out. Besides, the model for predicting SiO2 content of experimental samples was established based on NDI. We tested and verified the practicality of the model. The results showed that the sensitive waveband of the three spectral indexes and SiO2 content were all located at 8.06 and 8.20 μm which was the left border of Reststrahlen Features. And the correlation coefficient of the three spectral indexes and SiO2 content were all above 0.9. The correlation between NDI and SiO2 content was the best. Moreover, the predictive residual of SiO2 content prediction model which was based on NDI was 3.57%. The prediction results of the model were ideal. We provide a new method for determining the SiO2 content of Anshan-type iron. The method has the advantages of low working strength, simplicity, efficiency and non-pollution nature. It has some certain guiding significance for remote sensing exploration.
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Received: 2017-07-11
Accepted: 2017-11-29
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Corresponding Authors:
LIU Shan-jun
E-mail: liusjdr@126.com
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[1] CHEN Ping, Lü Xian-jun, SUN Li-jun(陈 平, 吕宪俊, 孙丽君). Metal Mine(金属矿山), 2009, 392(2): 102.
[2] Liu D W, Li L, Sun Y. Icarus, 2015, 253: 41.
[3] YANG Ri-hong, LI Zhi-zhong, CHEN Xiu-fa, et al(杨日红, 李志忠, 陈秀发, 等). Journal of Geo-Information Science(地球信息科学学报), 2012, 14(3): 411.
[4] YAN Bo-kun, LIU Sheng-wei, WANG Run-sheng(闫柏琨, 刘圣伟, 王润生). Geological Bulletin of China(地质通报), 2006, 25(5): 693.
[5] LIU Xuan, FAN Hong-rui, HU Fang-fang, et al(刘 玄, 范宏瑞, 胡芳芳, 等). Geological Review(地质论评), 2015, 1: 45.
[6] Salisbury J W, Walter L S, D’Aria D. US Geal. Surv. Open File Report, 1988, 88: 686.
[7] WANG Jun, LI Ting-dong, GENG Shu-fang, et al(王 军, 李廷栋, 耿树方, 等). Acta Geoscientica Sinica(地球学报), 2010, 31(3): 423.
[8] Copper B L, Salisbury J W, Killen R M. Journal of Geophysical Research, 2002, 107(E4): 1.
[9] XIAO Qing, LIU Qin-huo, LI Xiao-wen, et al(肖 青, 柳钦火, 李小文, 等). Journal of Infrared and Millimeter Waves(红外与毫米波学报), 2003, 22(5): 372.
[10] ZHANG Yong, LIANG Guang-lin, WU Qian-yi, et al(张 永, 梁广林, 吴倩怡, 等). Acta Petrologica Sinica(岩石学报), 2010, 26(10): 2997.
[11] Ninomiya Y, Fu B, Cudahy T J. Remote Sensing of Environment, 2005, 99: 127.
[12] Greenhagen B T, Lucey P G, Wyatt M B, et al. Science, 2010, 329(5998): 1507.
[13] YANG Chang-bao, ZHU Qun, JIANG Qi-gang, et al(杨长保, 朱 群, 姜琦刚, 等). Geology and Exploration(地质与勘探), 2009, 45(6): 692.
[14] GUO Bang-jie, ZHANG Jie-lin, WU Ding, et al(郭帮杰, 张杰林, 武 鼎, 等). Science Technology and Engineering(科学技术与工程), 2017, 17(3): 154. |
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