Grade engineering at Gramalote gold deposit, Colombia: an example of industry – research collaboration, Part 2

Grade Engineering® (GE) involves a range of integrated technologies and operating protocols for improving effective feed grade through early rejection of low value material prior to costly processing activities and has the potential to transform the economics of large, low grade, mining operations. Extensive GE test work by AngloGold Ashanti (AGA) and the Cooperative Resource Centre for Optimising Resource Extraction (CRCORE) determined that the Gramalote gold deposit in Colombia is highly amenable to implementation of grade-by-size screening to upgrade the ore at a production scale. To understand the relationships between geology and the upgrade response drill core and bulk samples were analysed at the Centre for Ore Deposit and Earth Sciences (CODES) as part of a larger CRCORE project to understand geological controls on grade-by-size deportment, i.e., the tendency for specific mineral phases to preferentially fractionate during breakage into different size fractions. This work is focussed on understanding the role of mineralogy and texture in influencing rock breakage and natural fractionation at a range of scales. Detailed logging of Gramalote drill core and mineralogical analyses including hyperspectral logging, portable and bench-scale X-ray fluorescence (XRF), plus low resolution Laser Ablation of intact drill core pieces, combined with data from site (drill logs, geochemistry, petrophysics) were used to evaluate the ore and alteration mineralogy, vein paragenesis and textural variability. This provided information on gold deportment and allowed the identification of favourable host veins. Bulk sample material from different ore grade zones was also analysed. A comparison with the results of GE testing shows that positive upgrade results are produced by rocks with abundant veins, particularly those associated with potassic and/or white mica alteration. This work indicates upgrade response can be predicted from drill core using data collected by a combination of automated systems including hyperspectral data, XRF, and detailed drill core logging.