Geometallurgy 2023
AMC Consultants is excited to attend the Geometallurgy conference on the 4-7 September in Stellenbosch of South Africa's Western Cape.
AMC's Ian Lipton co-authored a paper being presented at the event as part of the conference, entitled "Modelling sphalerite mineral chemistry – towards a geometallurgical model for Gamsberg North". The paper was a collaboration with other writers from Mountain Mining, Vedanta Zinc International, Aggeney, University of Cape Town, all of whom are based in the South African and Australian regions.
Modelling sphalerite mineral chemistry – towards a geometallurgical model for Gamsberg North
W Price,1 A Molifie,2 K Pillay,2 K Moses,3 I Lipton,4 P Greenhill,4 L Torres,4 P Spathelf,5 M Becker2*
1 Black Mountain Mining, Vedanta Zinc International, Aggeneys, South Africa
2 Centre for Minerals Research, Department of Chemical Engineering, University of Cape Town, South Africa
3 Department of Geological Sciences, University of Cape Town, South Africa
4 AMC Consultants Pty Ltd, Melbourne, Australia
5 Gamsberg Operations, Black Mountain Mining, Vedanta Zinc International, Aggeneys, South Africa
Gamsberg North is a world class Zn ± Pb and Ag deposit located in the Northern Cape Province of South Africa that came into production in 2018. It is a geologically, geochemically and mineralogically complex ore deposit, with its primary variability arising from the complex Mn and Fe deportment. The primary ore mineral, sphalerite, exhibits a range of compositions depending on the degree of Fe and Mn substitution for Zn in the sphalerite crystal lattice. Mn in the concentrate significantly impacts the price and saleability of the final concentrate product. To ensure that targets for the Mn grade in the final sphalerite concentrate are met, complex blending of the run of mine feed ore is required. This blending in turn requires upfront knowledge of the sphalerite composition mineral distribution spatially throughout the ore body. This paper describes the programmatic approach that was taken to acquire this ore body knowledge using a range of analytical techniques (QEMSCAN, SEM-EDS/WDS, EPMA, XRD, Raman, ICP-OES) complemented by the application of machine learning algorithms to develop predictive models that can be applied to the drill hole database. Developing a comprehensive understanding of the mineralogy and associated sphalerite compositional variation across the orebody and host rocks is fundamental to managing various aspects of mining, through ore blending to the processing of the ore, to optimise the saleable concentrate.
GMUSG Conference and Trade Expo 2023
Read our case study
Subscribe for the latest news & events