Integrating supervised machine learning and geostatistics for enhanced geometallurgical models
J. L. Deutsch, R.M. Barnett, and C.V. Deutsch
Applying machine learning to predict the impact of changing iron ore properties on blast furnace performance
T.R. Scott, C. Gous, J. Muller, and J.L. Deutsch
Application of data analytics to predict gold recovery from glycine-based bottle roll tests using Witwatersrand supergroup composite ore
V. Notole, G. T. Nwaila, J.E. Bourdeau and S.E. Zhang
The novel application of continuous wavelet tessellation and data mosaic method to assess relationships between material fingerprints and time series plant response actuals at Tropicana gold mine
L. M. Cloete, J. R. van Duijvenbode, M. S. Shishvan and M. W. N. Buxton
Application and integration of a geometallurgical model and integration thereof, with quantitative simulation at Orapa Mine
O. Gilika and P. van der Westhuyzen
Modelling sphalerite mineral chemistry – towards a geometallurgical model for Gamsberg North
W Price, A Molifie, K Pillay, K Moses, I Lipton, P Greenhill, L Torres, P Spathelf, and M Becker
Phase II: Continuous improvement of geometallurgy at Namakwa Sands Mine
K.D. Tshivhase and C. Philander
Developing geometallurgical proxies to predict ore hardness: A mineral sands case study
M. Sikushumane, A. van der Westhuizen, C. Philander , and M. Becker
Suitability of data repurposing - assessing sedimentological data as predictors for gold grade estimation in the Witwatersrand Goldfields
T. Mombe, G.T. Nwaila, and S.E. Zhang
A new approach to targeting drilling locations: quantifying geological knowledge in drilling campaigns using model-based design of experiments approaches
P. Deussen and F. Galvanin
Analysis of digital core to harness the upside of emergent selection and sorting technologies
S. Coward, K. Crossling, L. Bray, S. Ayrault
Application of electrical resistivity tomography as a physicochemical tool for tailings valorisation and remediation strategies
M. Manuel, M. van Schoor, J. Amaral Filho, and S. Harrison
Determining the lowest quantity of variables required for geometallurgical machine learning-based modelling: A review
E.E. Mack, B.P. von der Heyden, M. Tadie, and T.M. Louw
Adoption of common data reporting standards for metallurgical, geometallurgical and mineralogical data
S.A. Brand, K.S. Turnock, D. Foley, and B.E. Mullen
Development of an implicit 3D geological modelling workflow as a basis for geometallurgical modelling: A case study of a pegmatite deposit
I. Cupido,1 M. Becker1, and G. Nwaila2