Comparative study of iron ore characterisation using a scanning electron microscope and optical image analysis

References

• AxioVision, 2006–2009. V 4·8·0·0, Carl Zeiss Imaging Solutions GmbH.
   Google Scholar
• Beukes NJ, Mukhopadhyay J and Gutzmer J. 2008. Genesis of high-grade iron ores of the Archean Iron Ore Group around Noamundi, India, Econ. Geol., 103, 365–386.
   Web of Science ®Google Scholar
• Bonifazi G. 1995. Digital multispectral techniques and automated image-analysis procedures for industrial ore modelling, Miner. Eng., 8, (7), 779–794.
   Web of Science ®Google Scholar
• Clout JMF. 2003. Upgrading processes in BIF-derived iron ore deposits: implications for ore genesis and downstream mineral processing, Appl. Earth Sci., 112, (1), 89–95.
   Google Scholar
• Clout JMF and Simonson BM. 2005. Precambrian iron formations and iron formation- hosted iron ore deposits, Econ. Geol., 100th Anniv. Vol., 643–679.
   Google Scholar
• Danti KJ, Cross KC and Halsall C. 1993. A high performance, low cost image analysis system for IBM compatible computers: practical applications in mineral processing and geology, in Programme and Abstracts, International Congress on Applied Mineralogy ‘93, Mineralogy in the Service of Mankind, Fremantle, WA, Australia, May–June, AusIMM, 47–49.
   Google Scholar
• Donskoi E, and Clout JMF. 2005. automated textural classification of iron ores using ‘Recognition’ – A specialised software package for studying iron ores. Proceedings, Iron Ore 2005, Fremantle, WA, Australia, September, AusIMM, 203–211.
   Google Scholar
• Donskoi E, Holmes RJ, Manuel JR, Campbell JJ, Poliakov A, Suthers SP and Raynlyn T. 2008a. Utilization of iron ore texture information for prediction of downstream process performance, Proceedings, 9th International Congress for Applied Mineralogy, Brisbane, Australia, September, 687–693 , AusIMM—Australian Institute of Mining and Metallurgy.
   Google Scholar
• Donskoi E, Manuel JR, Clout JMF and Zhang Y. 2007. Mathematical modeling and optimization of iron ore sinter properties, Israel J. Chem., 47, 373–379.
   Web of Science ®Google Scholar
• Donskoi E, Poliakov A, Manuel J, Peterson M and Hapugoda S. 2013. Industrial Strength Optical Image Analysis System – Mineral4/Recognition4, Iron Ore 2013, Perth, WA, Australia, August, AusIMM, 227–241.
   Google Scholar
• Donskoi E, Poliakov A, Manuel JR and Raynlyn TD. 2010. Advances in optical image analysis and textural classification of iron ore fines, Proceedings, XXV International Mineral Processing Congress – IMPC2010, Brisbane, Australia, September, 45, AusIMM—Australian Institute of Mining and Metallurgy.
   Google Scholar
• Donskoi E, Poliakov A, Manuel JR and Raynlyn T. 2008b. Development of optical image analysis techniques for improved mineral and texture identification of iron ores, Proceedings, Automated Mineralogy 2008, Brisbane, Australia, September.
   Google Scholar
• Donskoi E, Suthers SP, Campbell JJ and Raynlyn T. 2008c. Modelling and optimization of hydrocyclone for iron ore fines beneficiation – using optical image analysis and iron ore texture classification, Int. J. Miner. Process., 87, 106–119 , AusIMM—Australian Institute of Mining and Metallurgy.
   Web of Science ®Google Scholar
• Donskoi E, Suthers SP, Campbell JJ, Raynlyn T and Clout JMF. 2006. Prediction of hydrocyclone performance in iron ore beneficiation using texture classification. Proceedings, XXIII International Mineral Processing Congress, Istanbul, Turkey, September 2006, 1897–1902 , AusIMM—Australian Institute of Mining and Metallurgy.
   Google Scholar
• Figueroa G, Möller K, Buhot M, Gloy G and Haberlah D. 2011. Proceedings, 10th International Congress for Applied Mineralogy (ICAM), Trondheim, Norway, August, 197–204 , Springer.
   Google Scholar
• Gomes ODM, Paciornik S and Iglesias JCA. 2010. A simple methodology for identifying hematite grains under polarized reflected light microscopy, in Proceedings of 17th International Conference on Systems, Signals and Image Processing – IWSSIP, 428–431, Rio de Janeiro, EdUFF.
   Google Scholar
• Gottlieb P, Wilkie G, Sutherland D, Ho-Tun E, Suthers S, Perera K, Jenkins B, Spencer S, Butcher A and Rayner J. 2000. Using quantitative electron microscopy for process mineralogy applications, J. Min., 24–25.
   Google Scholar
• Iglesias JCA, Gomes ODM and Paciornik S. 2011. Automatic recognition of hematite grains under polarized reflected light microscopy through image analysis, Miner. Eng., 24, (12), 1264–1270.
   Web of Science ®Google Scholar
• König U. 2013. Application of X-ray diffraction to iron ores- potential implications for grade control and downstream processing, Proceedings, Iron Ore 2013, Perth, WA, Australia, August, AusIMM, 275–280.
   Google Scholar
• Lane GR, Martin C and Pirard E. 2008. Techniques and applications for predictive metallurgy and ore characterization using optical image analysis, Miner. Eng., 21, 568–577.
   Web of Science ®Google Scholar
• Maddren J, Ly CV, Suthers SP, Butcher AR, Trudu AG and Botha PWSK. 2007. A new approach to ore characterisation using automated quantitative mineral analysis. Proceedings, Iron Ore 2007, Perth, Australia, August, AusIMM, 131.
   Google Scholar
• Morris RC. 1980. A textural and mineralogical study of the relationship of iron ore to banded iron-formation in the Hamersley Iron Province of Western Australia, Econ. Geol., 75, 184–209.
   Web of Science ®Google Scholar
• Pirard E, Lebichot S and Krier W. 2007. Particle texture analysis using polarized light imaging and grey level intercepts, Int. J. Miner. Process., 84, 299–309.
   Web of Science ®Google Scholar
• Poliakov A and Donskoi E. 2014. Automated relief-based discrimination of non-opaque minerals in optical image analysis, Miner. Eng., 55, 111–124.
   Web of Science ®Google Scholar
• Slayter EMicroscope. 1998. Grolier Multimedia Encyclopedia Online. Grolier.
   Google Scholar
• Spencer LJ. 1919. Mineralogical characters of Turite ( = turgite) and some other iron-ores from Nova Scotia, Mineralog. Mag., 18, 339–348.
   Google Scholar
• Zhang J and Subasinghe N. 2013. Prediction of mineral liberation characteristics of comminuted particles of high grade ores, Miner. Eng., 49, 68–76.
   Web of Science ®Google Scholar