Hostname: page-component-76fb5796d-qxdb6 Total loading time: 0 Render date: 2024-04-25T12:08:44.756Z Has data issue: false hasContentIssue false
  • English
  • Français

Automated Electron Microscopy for Mineralogical Characterization

Published online by Cambridge University Press:  28 February 2012

Rolando Lastra
Rolando Lastra*
Affiliation:
Department of Natural Resources Canada, (2011). All rights reserved CANMET, 555 Booth St., Ottawa, Ontario, K1A 0G1, CANADA
Get access

Abstract

Full automation of electron microscopy is now a mature technology that is being applied internationally mainly for mineralogical characterization. This technology has increased the speed and reliability of the characterization of ores and mineral processing products. It allows developing the most appropriate beneficiation technology for a new ore body. It helps to determine the potential, the optimization and the limitations of mineral concentrator plants. It can also be applied to the betterment of the environmental management of the metallurgical residues. This presentation will discuss the main approaches for fully automated electron microscopy. Additionally, an application case is presented, focusing on the characterization of complex ore of rare earth minerals.

Type
Research Article
Information
MRS Online Proceedings Library (OPL) , Volume 1380 , 2012 , imrc2011-1380-s21-full007
Copyright
Copyright © Materials Research Society 2012

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

1.Tarquini, S., & Pirard, E. (1996): Improved phase segmentation using RGB imaging. Proc. Joint Meeting of Belgian Soc. Microscopy (BVM) and Netherlands Soc. Microbiol (NVM), Gent, Belgium, pp 91–93.Google Scholar
2.Pirard, E. (1999): Optimal acquisition of video images in reflected light microscopy. Microscopy and analysis, July 1999, pp19–21.Google Scholar
3.Serranti, S. (1998): Le mineralizzazioni “Rubane” e masiva del giacimento a Cu-Sn di Corvo (neves-Corvo, Portogallo): studio metallografico e geochimico, implicazioni genetiche e applicazione di tecniche di análisis d’imagine. Thesis, Dottorato di Ricerca X Ciclo, Universita’ Degli Study di Roma “La Sapienza”, Dipartimento de Scienze della Terra.Google Scholar
4.Pirard, E. (2008): “Microscopia optica & Mineralogía optica cuantitativa” (Optical microscopy & Quantitative optical mineralogy” In Mineralogía applicada a los processos metalúrgicos (Mineralogy applied to metallurgical processes). Short Course. Pontificia Universidad Católica del Perú.Google Scholar
5.Grant, G., Hall, J.S.Reaid, A.F. & Zuiderwyk, M.A. (1976): Multi-compositional particle characterization using a SEM microprobe. Scanning Electron Microscopy, Chicago, ITT Research institute, III, pp. 401-40.8Google Scholar
6.Gu, Y. (2003): Automated scanning electron microscope based mineral liberation analysis. Journal of Minerals & Materials Characterization & Engineering, Vol. 2, No. 1, pp. 33–4.Google Scholar
7.Petruk, W. (1987): The MP-SEM-IPS image analysis system. CANMET, Dept. of Eneragy Mines and Resources, Ottawa, Canada. CANMET report 87-1E. 28p.Google Scholar
8.Lastra, R., Petruk, W. & Wilson, J. (1998): “Image analysis techniques and applications to mineral processing.” In Modern Approaches to Ore and Environmental Mineralogy, Mineralogical Association of Canada, Vol. 27, pp 327–366.Google Scholar
9.Lastra, R., Wilson, J.M.D. & Cabri, J.J. (1999): Automated gold search and applications in process mineralogy. Trans. Instn. Min. Metall. Vol 108, pp c75–c84.Google Scholar
10.Lastra, R.Price, J, Cabri, L.J., Rudashevsky, N.S., Rudashevsky, V.N. & McMahon, G. (2005): Gold characterization of a sample from Malartic East (Québece) using concentration by hydroseparation. In Proceedings Treatment of gold ores (Eds. Deschênes, G, Hodouin, D & Lorenzen, L). 44 Annual Conference of Metallurgists. Calgary, Alberta, Canada, 2005.Google Scholar
11.Castroviejo, R., Chacón, E., Múzquiz, C. & Tarquini, S. (1999): A preliminary image analysis characterization of massive sulphide ores from the SW Iberian pyrite belt (Spain). Proc. Geovision 99, Liège, Belgium, pp. 37-40.Google Scholar
12.Castroviejo, R.Berrezueta, E. & Lastra, R. (2002): Microscopic digital image analysis of gold ores: a critical test of methodology, comparing reflected light and electron microscopy. Minerals & Metallurgical Processing, Vol. 19, No. 2, 2002, pp 102–109.Google Scholar
13.Gy, P., (1979). Sampling of particulate materials: Theory and practice. Amsterdam ; New York; New York: Elsevier Scientific Pub. Co.; distributors for the U.S. and Canada, Elsevier/North-Holland.Google Scholar
14.Lastra, R. & Ownes, D. (1995)a: Mineralogical analysis of ore specimens from the rare earth deposit of Dodgex Ltd, Part I. CANMET Division report 95–025(CR). Natural Resources Canada.Google Scholar
15.Lastra, R. & Ownes, D. (1995)b: Mineralogical analysis of ore specimens from the rare earth deposit of Dodgex Ltd, Part II. CANMET Division report 95–043(CR). Natural Resources Canada.Google Scholar
16.Petruk, W. (1986): Predicting and measuring mineral liberation in ores and mill products and effect of mineral textures and grinding methods on mineral liberation. Process Mineralogy VI (Ed. Hagni, R.D.). AIME/SME, Warrendale. PA., pp 393–403.Google Scholar
17.Lastra, R. (2007) “Seven Practical application cases of liberation analysis.” R.P. King Special Issue of the International Journal of Mineral Processing, Vol. 84, Nos 1-4, 19 October 2007. pp 337–347.Google Scholar