|
|
|
|
|
|
Effect of Particle Size on Reflectance Spectra of Anshan Iron Ore |
WANG Dong, LIU Shan-jun*, QI Yu-xin, LIU Hai-qi |
School of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China |
|
|
Abstract Generally, the influence factors of the reflectance spectrum of rocks and minerals can be divided into the decisive factors related to composition and content, and the variant factors, including the particle size, roughness, observation angle and surface morphology. The study is focused on the relationship between the reflectance spectrum and the particle size of Anshan iron ore. Firstly, the reflectance spectra of the two main iron ores (hematite ore and magnetite ore) with different particle sizes are obtained by SVC HR-1024. Then, the influence of particle size on the reflectance spectra of both hematite and magnetite is analyzed. The sensitive waveband and stable waveband of the reflectance spectra related to particle size are extracted. The study indicates the following results. Firstly, the effects of particle size on the reflectance spectra of hematite and magnetite are different. For the hematite, the reflectance decreases with the particle size increase when the particle size is in the range of 0.03 to 1 mm. The effects are different in different waveband for the reflectance spectra of hematite. In the wave range of 350~550 nm, the particle size’s effect can be, neglected and this waveband can be regarded as the stable waveband. The influence is weak in the range of 550~950 nm and become obvious in the 950~1 250 nm range. In the range of 1 250~2 500 nm, the reflectance changes obviously with the particle size. Therefore, this waveband can be regarded as a sensitive waveband. However, when the particle size of the hematite is larger than 1 mm, the particle size’s effect decreased obviously without correlation. For the magnetite, the reflectance changes weakly with an amplitude of less than 3% when the particle size is in the range of 0.03 to 4 mm. There is no correlation between the reflectance and the particle size. This study revealed the quantitative relationship between the reflectance spectrum and the particle size for Anshan iron ore. The results can provide the foundation for improving the inversion accuracy of the ore grading for the Anshan iron.
|
Received: 2020-05-08
Accepted: 2020-09-11
|
|
Corresponding Authors:
LIU Shan-jun
E-mail: liusjdr@126.com
|
|
[1] Walid S, Mourtada E A, Reinhard G. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2015, 136(5): 1816.
[2] Feng Q C, Wen S M, Bai X, et al. Minerals Engineering, 2019, 137: 1.
[3] Zhao W J, Liu D W, Feng Q C, et al. Minerals Engineering, 2019, 141: 105846.
[4] YANG Bai-lin, WANG Xing-li, WANG Zhong-sheng(杨柏林, 王兴理, 王忠圣). Geochimica(地球化学), 1987,(1): 89.
[5] Salisbury J W, Wald A. ICARUS, 1992, 96(1): 121.
[6] Okin G S, Painter T H. Remote Sensing of Environment, 2004, 89: 272.
[7] WANG Jin-hua, CAO Lan-jie, BAI Yang, et al(汪金花, 曹兰杰, 白 洋, 等). Multipurpose Utilization of Mineral Resources(矿产综合利用), 2019,(2): 128.
[8] Carli C, Roush T L, Pedrazzi G. ICARUS, 2016, 266: 267.
[9] Hatcher A, Hill P, Grant P, et al. Marine Geology, 2000, 168: 115.
[10] MA Chuang, SHEN Guang-rong, WANG Zi-jun, et al(马 创, 申广荣, 王紫君, 等). Chinese Journal of Soil Science(土壤通报), 2015, 46(2): 292.
[11] WANG Yan-xia, WU Jian, ZHOU Liang-guang, et al(王延霞, 吴 见, 周亮广, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2015, 35(3): 803.
[12] WANG Tao(王 涛). Science Technology and Engineering(科学技术与工程), 2017, 17(2): 234.
[13] Mi J R, Zhang L D, Zhao L L, et al. Frontiers of Optoelectronics,2013, 6(2): 216.
[14] Vernazza P, Carry B, Emery J, et al. ICARUS, 2010, 207: 800. |
[1] |
HE Qing-yuan1, 2, REN Yi1, 2, LIU Jing-hua1, 2, LIU Li1, 2, YANG Hao1, 2, LI Zheng-peng1, 2, ZHAN Qiu-wen1, 2*. Study on Rapid Determination of Qualities of Alfalfa Hay Based on NIRS[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3753-3757. |
[2] |
LIU Hao-dong1, 2, JIANG Xi-quan1, 2, NIU Hao1, 2, LIU Yu-bo1, LI Hui2, LIU Yuan2, Wei Zhang2, LI Lu-yan1, CHEN Ting1,ZHAO Yan-jie1*,NI Jia-sheng2*. Quantitative Analysis of Ethanol Based on Laser Raman Spectroscopy Normalization Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3820-3825. |
[3] |
LIU Wei1, 2, ZHANG Peng-yu1, 2, WU Na1, 2. The Spectroscopic Analysis of Corrosion Products on Gold-Painted Copper-Based Bodhisattva (Guanyin) in Half Lotus Position From National Museum of China[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3832-3839. |
[4] |
LIN Hong-jian1, ZHAI Juan1*, LAI Wan-chang1, ZENG Chen-hao1, 2, ZHAO Zi-qi1, SHI Jie1, ZHOU Jin-ge1. Determination of Mn, Co, Ni in Ternary Cathode Materials With
Homologous Correction EDXRF Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3436-3444. |
[5] |
HUANG Li, MA Rui-jun*, CHEN Yu*, CAI Xiang, YAN Zhen-feng, TANG Hao, LI Yan-fen. Experimental Study on Rapid Detection of Various Organophosphorus Pesticides in Water by UV-Vis Spectroscopy and Parallel Factor Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3452-3460. |
[6] |
LI Zhong-bing1, 2, JIANG Chuan-dong2, LIANG Hai-bo3, DUAN Hong-ming2, PANG Wei2. Rough and Fine Selection Strategy Binary Gray Wolf Optimization
Algorithm for Infrared Spectral Feature Selection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3067-3074. |
[7] |
LIU Shu1, JIN Yue1, 2, SU Piao1, 2, MIN Hong1, AN Ya-rui2, WU Xiao-hong1*. Determination of Calcium, Magnesium, Aluminium and Silicon Content in Iron Ore Using Laser-Induced Breakdown Spectroscopy Assisted by Variable Importance-Back Propagation Artificial Neural Networks[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3132-3142. |
[8] |
LIU Wen-bo, LIU Jin, HAN Tong-shuai*, GE Qing, LIU Rong. Simulation of the Effect of Dermal Thickness on Non-Invasive Blood Glucose Measurement by Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2699-2704. |
[9] |
KONG De-ming1, LIU Ya-ru1, DU Ya-xin2, CUI Yao-yao2. Oil Film Thickness Detection Based on IRF-IVSO Wavelength Optimization Combined With LIF Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2811-2817. |
[10] |
ZHAO Yu-wen1, ZHANG Ze-shuai1, ZHU Xiao-ying1, WANG Hai-xia1, 2*, LI Zheng1, 2, LU Hong-wei3, XI Meng3. Application Strategies of Surface-Enhanced Raman Spectroscopy in Simultaneous Detection of Multiple Pathogens[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2012-2018. |
[11] |
CHENG Xiao-xiang1, WU Na2, LIU Wei2*, WANG Ke-qing2, LI Chen-yuan1, CHEN Kun-long1, LI Yan-xiang1*. Research on Quantitative Model of Corrosion Products of Iron Artefacts Based on Raman Spectroscopic Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2166-2173. |
[12] |
YAN Xue-jun1, ZHOU Yang2, HU Dan-jing1, YU Dan-yan1, YU Si-yi1, YAN Jun1*. Application of UV-VIS Diffuse Reflectance Spectrum, Raman and
Photoluminescence Spectrum Technology in Nondestructive
Testing of Yellow Pearl[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1703-1710. |
[13] |
CHEN Xiao-li1, LI You-li1, LI Wei3, WANG Li-chun1, GUO Wen-zhong1, 2*. Effects of Red and Blue LED Lighting Modes on Spectral Characteristics and Coloring of Tomato Fruit[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1809-1814. |
[14] |
CHEN Rui1, WANG Xue1, 2*, WANG Zi-wen1, QU Hao1, MA Tie-min1, CHEN Zheng-guang1, GAO Rui3. Wavelength Selection Method of Near-Infrared Spectrum Based on
Random Forest Feature Importance and Interval Partial
Least Square Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 1043-1050. |
[15] |
DENG Xiao-jun1, 2, MA Jin-ge1, YANG Qiao-ling3, SHI Yi-yin1, HUO Yi-hui1, GU Shu-qing1, GUO De-hua1, DING Tao4, YU Yong-ai5, ZHANG Feng6. Visualized Fast Identification Method of Imported Olive Oil Quality Grade Based on Raman-UV-Visible Fusion Spectroscopy Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 1117-1125. |
|
|
|
|