Development of a predictive geometallurgical recovery model for the La Colosa, Porphyry Gold Deposit, Colombia
Leichlister, S and Hunt, J and Berry, R and Keeney, L and Montoya, PA and Chamberlain, V and Jahoda, R and Drews, U, Development of a predictive geometallurgical recovery model for the La Colosa, Porphyry Gold Deposit, Colombia, Proceedings of the 1st International Geometallurgy Conference (GeoMet 2011), 05-07 September 2011, Brisbane, pp. 85-92. ISBN 9781921522499 (2011) [Refereed Conference Paper]
La Colosa, Colombia is a large gold-porphyry deposit currently undergoing feasibility studies by AngloGold Ashanti. This period of development allows the opportunity to apply innovative and emerging testing and modelling methods to provide a predictive geometallurgical recovery model for the deposit. By partnering with the AMIRA P843A Geometallurgical Mapping and Mine Modelling (GeMIII) research project, the geological and mineralogical data are analysed with respect to liberation and recovery methods. This will allow the variability in the geology and mineralogy of the deposit and key relationships recognised in the data to be included in the development of a model to help predict recoveries of the gold. Many aspects of the gold mineralisation such as gold paragenesis, associations, grain size and texture are determined and analysed using optical microscopy, Mineral Liberation Analysis (MLA), and laser ablation (LA-ICP-MS). At La Colosa the gold has been located in sulfides (eg pyrite, pyrrhotite, and chalcopyrite), silicates, along silicate phase boundaries, and among the pyrite-rich intermediate argillic alteration. Further testing and analysis will also determine if ‘invisible’ gold is present in the pyrite, pyrrhotite, and chalcopyrite. Recovery methods can also be determined and tested after combining the results of mineralogy tests with other data (eg results of comminution tests). Indices for the mineralisation and recovery are developed using image analysis and analytical testing. These indices are correlated with recovery data from the company to develop a predictive block model for recovery. The goal is to develop cost-effective, efficient methods to analyse the mineralogy and geology of the deposit; understand the variability in the mineralogy, geology, and recovery; and construct a predictive geometallurgical recovery model.