智能矿石拣选技术及设备的发展综述

罗仙平; 何坤忠; 张燕; 何鹏宇; 张永兵

doi:10.1007/s12613-022-2477-5

Volume 29 Issue 9

Sep. 2022

Turn off MathJax

图(4)

数据统计

计量

文章访问数: 3497
HTML全文浏览量: 1158
PDF下载量: 411
被引次数: 0

文章导航 > 矿物冶金与材料学报（英文） > 2022 > 29(9): 1647-1655

Xianping Luo, Kunzhong He, Yan Zhang, Pengyu He, and Yongbing Zhang, A review of intelligent ore sorting technology and equipment development, Int. J. Miner. Metall. Mater., 29(2022), No. 9, pp. 1647-1655. https://doi.org/10.1007/s12613-022-2477-5

Cite this article as:

Xianping Luo, Kunzhong He, Yan Zhang, Pengyu He, and Yongbing Zhang, A review of intelligent ore sorting technology and equipment development, Int. J. Miner. Metall. Mater., 29(2022), No. 9, pp. 1647-1655. https://doi.org/10.1007/s12613-022-2477-5

Citation:

PDF( 724 KB)

引用本文 PDF XML SpringerLink

特约综述

智能矿石拣选技术及设备的发展综述

1)
江西理工大学江西省钨资源高效开发与应用重点实验室, 赣州 341000, 中国
2)
江西理工大学江西省矿冶环境污染控制重点实验室, 赣州 341000, 中国
3)
江西理工大学资源与工程学院, 赣州 341000, 中国

通讯作者:
罗仙平 E-mail: luoxianping9491@163.com

文章亮点

（1）系统地研究了影响智能分选设备分选效率的因素，主要包括矿石信息识别技术、设备分选执行器和信息处理算法。

（2）总结了近年来智能分拣设备取得的成果。讨论了颜色矿石拣选机、XRT矿石拣选机、双能XRT矿石拣选机、XRF矿石拣选机和NIR矿石拣选机等设备的发展及应用。

（3）展望了LIBS、PGNAA、在线FTIR和磁共振等在线快速元素分析技术在智能拣选设备的应用。

（4）信息处理算法的应用和改进，如峰面积法、主成分分析法、人工神经网络法、PLS法和MCLLS等，将进一步推动智能分拣设备的发展。

中文摘要

在世界矿石资源日益贫细杂化、市场竞争日益激烈和环境污染问题严重的背景下，矿业发展受到了强烈制约。智能拣选设备的发展可以提高资源的利用率，提高企业的经济效益，增加入选矿石品位、降低磨矿成本及尾砂生产量。智能拣选设备的长期应用发现影响分选效率的因素主要有矿石信息识别技术、设备分选执行机构、信息处理算法等。这些因素的精确、高速运行是智能拣选设备分选效率的保障。近几年来，智能拣选设备的发展也取得了许多成果。根据矿物的特征信息不同，为了保证设备选别的精确性的同时提高设备分选效率，进而相继研发了颜色分选机、X射线透射分选机（XRT）、双能X射线透射分选机（DE-XRT）、X射线荧光分选机（XRF）以及近红外分选机（NIR）等。随着矿业自动化脚步的不断推进，在线元素快速分析系统将成为未来智能拣选设备发展的必然趋势。激光诱导击穿光谱技术（LIBS）、瞬发γ中子活化分析技术（PGNAA）、在线傅立叶变换红外光谱技术（FTIR）、核磁共振（MR）技术及信息处理算法，如峰面积法、主成分分析法、人工神经网络法、偏最小二乘法（PLS）、蒙特卡洛谱库最小二乘法等的应用和提升，将使智能拣选选矿设备的发展步入一个新的台阶。

关键词:

Invited Review

A review of intelligent ore sorting technology and equipment development

+ Author Affiliations

Abstract

Abstract

Under the background of increasingly scarce ore worldwide and increasingly fierce market competition, developing the mining industry could be strongly restricted. Intelligent ore sorting equipment not only improves ore use and enhances the economic benefits of enterprises but also increases the ore grade and lessens the grinding cost and tailings production. However, long-term research on intelligent ore sorting equipment found that the factors affecting sorting efficiency mainly include ore information identification technology, equipment sorting actuator, and information processing algorithm. The high precision, strong anti-interference capability, and high speed of these factors guarantee the separation efficiency of intelligent ore sorting equipment. Color ore sorter, X-ray ore transmission sorter, dual-energy X-ray transmission ore sorter, X-ray fluorescence ore sorter, and near-infrared ore sorter have been successfully developed in accordance with the different characteristics of minerals while ensuring the accuracy of equipment sorting and improving the equipment sorting efficiency. With the continuous improvement of mine automation level, the application of online element rapid analysis technology with high speed, high precision, and strong anti-interference capability in intelligent ore sorting equipment will become an inevitable trend of equipment development in the future. Laser-induced breakdown spectroscopy, transient γ neutron activation analysis, online Fourier transform infrared spectroscopy, and nuclear magnetic resonance techniques will promote the development of ore sorting equipment. In addition, the improvement and joint application of additional high-speed and high-precision operation algorithms (such as peak area, principal component analysis, artificial neural network, partial least squares, and Monte Carlo library least squares methods) are an essential part of the development of intelligent ore sorting equipment in the future.
- intelligent ore sorting technology;
- sorting equipment;
- separation efficiency;
- online element rapid analysis technology

FullText(HTML)

Supplements(0)

References(65)

References

[1]	L. Panda, P.K. Banerjee, S.K. Biswal, R. Venugopal, and N.R. Mandre, Artificial neural network approach to assess selective flocculation on hematite and kaolinite, Int. J. Miner. Metall. Mater., 21(2014), No. 7, p. 637. doi: 10.1007/s12613-014-0952-3
[2]	G. Qian, On the development and utilization of low grade mineral resources, China Met. Bulletin., 2020, No. 1, p. 49.
[3]	T. Henckens, Scarce mineral resources: Extraction, consumption and limits of sustainability, Resour. Conserv. Recycl., 169(2021), art. No. 105511. doi: 10.1016/j.resconrec.2021.105511
[4]	J. Lessard, J. de Bakker, and L. McHugh, Development of ore sorting and its impact on mineral processing economics, Miner. Eng., 65(2014), p. 88. doi: 10.1016/j.mineng.2014.05.019
[5]	X.P. Luo, X.H. Ning, T. Wang, P.C. Wang, and P.Y. He, Development and application of intelligent picking technology, Met. Mine, 2019, No. 7, p. 113.
[6]	B. Sun, J.T. Dai, K.K. Huang, C.H. Yang, and W.H. Gui, Smart manufacturing of nonferrous metallurgical processes: Review and perspectives, Int. J. Miner. Metall. Mater., 29(2022), No. 4, p. 611. doi: 10.1007/s12613-022-2448-x
[7]	H. Wotruba and C. Robben, Sensor-based ore sorting in 2020, Automatisierungstechnik, 68(2020), No. 4, p. 231. doi: 10.1515/auto-2019-0060
[8]	J. Lessard, W. Sweetser, K. Bartram, J. Figueroa, and L. McHugh, Bridging the gap: Understanding the economic impact of ore sorting on a mineral processing circuit, Miner. Eng., 91(2016), p. 92. doi: 10.1016/j.mineng.2015.08.019
[9]	F. Zheng, Development of photoelectric sorter abroad, Nonferrous Met. Mineral Process., 1980, No. 2, p. 38.
[10]	T. Ding, H.P. Xu, J.S. Wang, D.R. Liu and Z.W. Wang, Research of online ore sorter based on vision recognition technology, Equip. Manuf. Technol., 2014, No. 7, p. 106.
[11]	Z.H. Wu, Ore pre concentration and discarding waste technology and selection of intelligent photoelectric beneficiation equipment, World Nonferrous Met., 2020, No. 16, p. 202.
[12]	Robben and Wotruba, Sensor-based ore sorting technology in mining—Past, present and future, Minerals, 9(2019), No. 9, art. No. 523. doi: 10.3390/min9090523
[13]	H. Wotruba and H. Harbeck, Sensor-based sorting, [in] Ullmann’s Encyclopedia of Industrial Chemistry, Wiley-VCH Verlag GmbH & Co. KgaA, Weinheim, 2012.
[14]	A. Cardenas-Vera, M. Hesse, R. Möckel, R. Gerhard Merker, T. Heinig, and Q.V. Phan, Investigation of Sensor-Based sorting and selective comminution for pre-concentration of an unusual parisite-rich REE ore, South Namxe, Vietnam, Miner. Eng., 177(2022), art. No. 107371. doi: 10.1016/j.mineng.2021.107371
[15]	X.S. Wu, W. Zhang, X.K. Zhao, X. Gao and W.B. Huang, New development trend of sensor-based ore pre-concentration technology, China Min. Mag., 31(2022), No. 6, p. 10.
[16]	D.Y. Zhu, D.Y. Liang, P.W. Shi, H.X. Ran, and H.L. Shang, Research and development of ore sorting technology and equipments, Min. Process. Equip., 44(2016), No. 7, p. 5.
[17]	H.J. Wang, X.P. Liu, G. Wang, and S. Han, Optimization of mobile manipulator sorting path based on improved genetic algorithm, J. Beijing Univ. Posts Telecommun., 43(2020), No. 5, p. 34.
[18]	Y.F. Wu, L.R. Gao, B. Zhang, H.N. Zhao, and J. Li, Real-time implementation of optimized maximum noise fraction transform for feature extraction of hyperspectral images, J. Appl. Remote Sensing, 8(2014), No. 1, art. No. 084797.
[19]	S.W. Wu, J. Yang, and G.M. Cao, Prediction of the Charpy V-Notch impact energy of low carbon steel using a shallow neural network and deep learning, Int. J. Miner. Metall. Mater., 28(2021), No. 8, p. 1309. doi: 10.1007/s12613-020-2168-z
[20]	M. Li, A. Caushaj, R. Silva, and D. Lowther, A neural network for electromagnetic based ore sorting, COMPEL Int. J. Comput. Math. Electr. Electron. Eng., 37(2018), No. 2, p. 691. doi: 10.1108/COMPEL-12-2016-0529
[21]	C. Bergmann, Developments in Ore Sorting Technologies, Council for Mineral Technology, Randburg, 2009, p. 75.
[22]	C.Y. Xu, The application of photoelectric color separator and the optimization in mineral separation process of some mine, World Nonferrous Met., 2016, No. 17, p. 31.
[23]	X.X. Shao and X.P. Ji, Separation of talc by photoelectric separation technology, Nonmetallic Min., 1989, No. 5, p. 22.
[24]	C. Robben, P. Condori, A. Pinto, R. Machaca, and A. Takala, X-ray-transmission based ore sorting at the San Rafael tin mine, Miner. Eng., 145(2020), art. No. 105870. doi: 10.1016/j.mineng.2019.105870
[25]	X.W. Yu, M. Yang, S.R. Mao, F.T. Yu and T. He, Research on application of X-ray separation technology in beneficiation of phosphate rock, Ind. Miner. Process., 49(2020), No. 9, p. 31.
[26]	N. Li, S.H. Zhang, H. Peng, L. Fu, and Y.J. Lu, Application practice and evaluation of photoelectric separation in a phosphate mining industry, Non Met. Mines, 41(2018), No. 2, p. 73.
[27]	N. Jia, Discarding experiment on XRT ray brainpower separator in a scheelite concentrator in Hunan Province, Mod. Min., 34(2018), No. 7, p. 154.
[28]	W. Peng and P.Y. He, Pre-selection test and practice of a antimony ore using the X-ray intelligent concentrator, Met. Mine, 2019, No. 9, p. 92.
[29]	W.P. Di and Z.H. Wu, Preconcentration and discarding technology of intelligent photoelectric dressing equipment, Nonferrous Met. Miner. Process., 2021, No. 1, p. 117.
[30]	H. Gao, J.Y. Wang, X.F. Zhang, and D.S. Zhao, Image processing design of mineral identification system based on dual-energy X-ray transmission, Nonferrous Met. Miner. Process., 2021, No. 1, p. 101.
[31]	M.G. Xu and X.D. Bao, Object classification suppressed thickness effect based on pseudo dual-energy X-ray transmission imaging system, Chin. J. Electron Devices, 30(2007), No. 1, p. 219.
[32]	L.N. Cui and X.Q. Peng, Test study on dual energy X-ray transmission pre-concentration of a low-grade lead–zinc mine in Guangxi, Min. Eng., 18(2020), No. 4, p. 30.
[33]	Y. Tong, Technical Amenability Study of Laboratory-scale Sensor-based Ore Sorting On a Mississippi Valley Type Lead–Zinc Ore [Dissertation], University of British Columbia, Vancouver, 2012, p. 41.
[34]	V. Rebuffel and J.M. Dinten, Dual-energy X-ray imaging: Benefits and limits, Insight Non Destr. Test. Cond. Monit., 49(2007), No. 10, p. 589. doi: 10.1784/insi.2007.49.10.589
[35]	G.Z. Li, B. Klein, C.B. Sun, and J. Kou, Lab-scale error analysis on X-ray fluorecence sensing for bulk ore sorting, Miner. Eng., 164(2021), art. No. 106812. doi: 10.1016/j.mineng.2021.106812
[36]	L.X. Li, G.Z. Li, H.Z. Li, G.Q. Li, D. Zhang, and B. Klein, Bench-scale insight into the amenability of case barren copper ores towards XRF-based bulk sorting, Miner. Eng., 121(2018), p. 129. doi: 10.1016/j.mineng.2018.02.023
[37]	G.Z. Li, B. Klein, C.B. Sun, and J. Kou, Applying Receiver–Operating–Characteristic (ROC) to bulk ore sorting using XRF, Miner. Eng., 146(2020), art. No. 106117. doi: 10.1016/j.mineng.2019.106117
[38]	W.Z. Yin, Y. Wu, Y.X. Han, et al., The theory and application of X-ray separation technology, China Min. Mag., 20(2011), No. 12, p. 88.
[39]	M.B. Liu, W.Z. Yin, T.X. Han, and Z.Q. Sun, The study on the X-ray separator and its pre-sorting test of the low grade ore of Mo and Ni, Min. Metall., 21(2012), No. 4, p. 26.
[40]	M. Dalm, M.W.N. Buxton, and F.J.A. Ruitenbeek, Ore–waste discrimination in epithermal deposits using near-infrared to short-wavelength infrared (NIR-SWIR) hyperspectral imagery, Math. Geosci., 51(2019), No. 7, p. 849. doi: 10.1007/s11004-018-9758-6
[41]	E. Gülcan, A novel approach for sensor based sorting performance determination, Miner. Eng., 146(2020), art. No. 106130. doi: 10.1016/j.mineng.2019.106130
[42]	Y.M. Liu, D. Chen, Q.F. Li, and K.X. Xu, Novel multi-scale modeling method for near infrared spectral measurement, Nanotechnol. Precis. Eng., 12(2014), No. 5, p. 381.
[43]	M.R. Robben, H. Wotruba, and J. Heizmann, Sensor-based separation of carbonates, [in] IMPC 2012: XXVI International Mineral Processing Congress, New Delhi, 2012, p. 04496.
[44]	S. Iyakwari, H.J. Glass, and P.B. Kowalczuk, Potential for near infrared sensor-based sorting of hydrothermally-formed minerals, J. Infrared Spectrosc., 21(2013), No. 3, p. 223. doi: 10.1255/jnirs.1049
[45]	H. Huang, W.H. Ye, T.Z. Xiong, P.L. Hu, and J.S. Huang, Zone division scrap non-ferrous metal recognition algorithm based on dual energy X-ray transmission, Mach. Build. Autom., 48(2019), No. 4, p. 26.
[46]	O. Udoudo, Modelling the Efficiency of an Automated Sensor-based Sorter [Dissertation], University of Exeter, Exeter, 2010.
[47]	N.G. Cutmore, Y. Liu, and A.G. Middleton, On-line ore characterisation and sorting, Miner. Eng., 11(1998), No. 9, p. 843. doi: 10.1016/S0892-6875(98)00071-5
[48]	M. Gaft, R. Reisfeld, and G. Panczer, Minerals radiometric sorting and online process control, [in] Modern Luminescence Spectroscopy of Minerals and Materials, Springer, Cham, 2015, p. 499.
[49]	T.F. Wang and J.X. Yang, Online intelligent sorting method of gold ore based on LIBS technology, [in] H. Haeri, ed., Materials in Environmental Engineering, De Gruyter, 2017, p. 753.
[50]	R. Noll, H. Bette, A. Brysch, M. Kraushaar, I. Mönch, L. Peter, and V. Sturm, Laser-induced breakdown spectrometry—Applications for production control and quality assurance in the steel industry, Spectrochim. Acta B: At. Spectrosc., 56(2001), No. 6, p. 637. doi: 10.1016/S0584-8547(01)00214-2
[51]	J.M. Vadillo, S. Palanco, M.D. Romero, and J.J. Laserna, Applications of laser-induced breakdown spectrometry (LIBS) in surface analysis, Fresenius J. Anal. Chem., 355(1996), No. 7-8, p. 909.
[52]	M. Sabsabi and P. Cielo, Quantitative analysis of aluminum alloys by laser-induced breakdown spectroscopy and plasma characterization, Appl. Spectrosc., 49(1995), No. 4, p. 499. doi: 10.1366/0003702953964408
[53]	Q.M. Peng, M.Y. Yao, M.H. Liu, Z.J. Lei, Y. Xu, and T.B. Chen, Analysis of metal element Cr in fresh orange leaf using laser-induced breakdown spectroscopy, Acta Agric. Univ. Jiangxiensis, 34(2012), No. 2, p. 397.
[54]	L.Z. Zahng, B.F. Ni, W.Z. Tian, et al., Status and development of prompt γ-ray neutron activation analysis, Atom. Energy Sci. Technol., 2005, No. 3, p. 282.
[55]	T.A. Aarhaug, A. Ferber, O. Kjos, and H. Gaertner, Online monitoring of aluminium primary production gas composition by use of Fourier-transform infrared spectrometry, [in] Light Metals 2014, Springer, Cham, 2014, p. 647-652.
[56]	Y. Qiu, B. Chen, and D.S. Jia, Development of application of infrared spectrometry, Environ. Sci. Surv., 27(2008), No. S1, p. 23.
[57]	X.F. Zhang, W. Ni, J.Y. Wu, and L.P. Zhu, Hydration mechanism of a cementitious material prepared with Si−Mn slag, Int. J. Miner. Metall. Mater., 18(2011), No. 2, p. 234. doi: 10.1007/s12613-011-0428-7
[58]	J. Moros, A. Gredilla, S. Fdez-Ortiz de Vallejuelo, A. de Diego, J.M. Madariaga, S. Garrigues, and M. de la Guardia, Partial least squares X-ray fluorescence determination of trace elements in sediments from the estuary of Nerbioi-Ibaizabal River, Talanta, 82(2010), No. 4, p. 1254. doi: 10.1016/j.talanta.2010.06.047
[59]	L.Q. Luo, G.Z. Ma, A. Ji, C.L. Guo, X.C. Zhan, and G.L. Liang, Neural cluster structure with multiple component prediction based on back error propagation in X-ray fluorescence spectrometry, J. Anal. Sci., 14(1998), No. 3, p. 177.
[60]	W.Y. Li, W.H. Ye, and T.Z. Xiong, Metal identification algorithm based on bp neural network for dual energy X-ray transmission, Nonferrous Met. Eng., 10(2020), No. 8, p. 124.
[61]	L.Q. Luo, L. Gan, X.J. Wu, A. Ji, and G.L. Liang, Correction of nonlinear matrix effects by using an algorithm of combining neural networks with fundamental parameters, Chin. J. Anal. Lab., 20(2001), No. 1, p. 1.
[62]	R.L. Man, X.N. Zhao, and A. Ji, Application of partial least-squares method in multicomponent analysis by portable radioisotope XRFS, Spectrosc. Spectr. Anal., 11(1991), No. 4, p. 50.
[63]	L.Q. Luo, G.Z. Ma, and A. Ji, Study of partial least squares regression and fundamental parameters method in X-ray fluorescence analysis, Chin. J. Anal. Chem., 20(1992), No. 9, p. 1074.
[64]	P. Urbański and E. Kowalska, Application of partial least-squares calibration methods in low-resolution EDXRS, X Ray Spectrom., 24(1995), No. 2, p. 70. doi: 10.1002/xrs.1300240209
[65]	L. Wielopolski and R.P. Gardner, Development of the detector response function approach in the least-squares analysis of X-ray fluorescence spectra, Nucl. Instrum. Methods, 165(1979), No. 2, p. 297. doi: 10.1016/0029-554X(79)90285-4