Validation of predictive flotation models in blended ores for concentrator process design

The use of geometallurgical modelling is becoming common in the design of a concentrator plant and during its operational phase. Predictive modelling aims to define optimized process parameters for the concentrator plant to accommodate the variability of the ore feed characteristics. The main objective of this study is to simulate the metallurgical response for distinct ore unit types and their blends using kinetic models derived from experimental flotation tests. Kinetic flotation tests, including both rougher and cleaner, were applied on four ore types containing different ratios of chalcopyrite, chalcocite and copper oxide minerals from Rich Metal Group's Madneuli copper-gold mine, Georgia. The metallurgical performance of complex copper ores Madnueli XI and V differs from each other significatively due to the different mineralogical distribution of copper sulphides and oxides. The blend of ore units in various ratios affects the efficiency of the process by decrementing the recovery and mass pull due to copper oxide-sulphate in the ore Madnueli V. HSC Chemistry (R) software was used to simulate the metallurgical response of blended ores based on mineral compositions of the ores, Klimpel kinetic rectangular distribution model and flowsheet model. Simulated and experimental results of copper grade, copper recovery and concentrate mass pull correlated well with R-2 values of over 0.95. These results demonstrate that the metallurgical response of ore blends can be simulated based on the flotation kinetics of distinct ore types. The use of predictive simulation can create savings during the test work phase and increase the flexibility and certainty in the process design stage.

Automated summary

The use of geometallurgical modelling is becoming common in the design and operation of concentrator plants. The main objective is to simulate the metallurgical response of ore blends using kinetic models derived from experimental flotation tests. Predictive simulation can create cost savings and increase flexibility and certainty in the process design stage.