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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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