This paper compares model predictions from a novel kinetic model with data from five fundamental single-mineral literature flotation datasets. The empirical correlations proposed by Safari and Deglon (2018) are modified to improve their robustness, requiring only a single best-fit regression coefficient per mineral type. Experimental and model-predicted rate constants were compared on a parity chart where a reasonable linear correlation was observed, with a gradient of 0.95 and an overall R² value of 0.97. Thereafter experimental and model-predicted trends from the flotation datasets were compared for particle size, contact angle, agitation, and gas flow rate. Model-predicted trends were reasonably accurate for most of the flotation datasets, but under-predicted the rate constant for larger particles for the data of Pyke (2004). In general model predictions were reasonably accurate, which is considered quite good, as these were obtained by fitting a single parameter per mineral type to several large flotation datasets, totaling 330 rate constants.

Sensor-based particulate ore sorting is a pre-concentration technique that sorts particles based on measurable physical properties, resulting in reduced energy consumption by removing waste prior to grinding. This study presents an integrated methodology to determine the potential for ore sorting based on intrinsic particle properties. The methodology first considers the intrinsic sortability based on perfect separation. Only intrinsically sortable ore is further assessed by determining the sensor-based sortability. The methodology is demonstrated using a case study based on a typical copper porphyry comminution circuit. The sorting duty identified for the case study was the removal of low-grade waste material from the pebble crusher stream at a suitable Cu cut-off grade. It was found that the ore had the potential to be sorted based on the intrinsic and ideal laboratory sensor sortability results but showed no potential to be sorted using industrial-scale sensors. The ideal laboratory XRF sensor results showed that around 40% of mass could be rejected as waste at copper recoveries above 80%. An economic analysis of the sortability tests showed that, at optimum separation conditions, the intrinsic, ideal sensor and industrial sensor sortability would result in an additional annual profit of ~$30 million, $21 million and $−7 million (loss), respectively.

The Brucutu iron ore mine (Minas Gerais, Brazil) is Vale‘s largest iron producing operation achieving around 21 million tons per annum. Evaluation of flotation performance is of high importance as even small gains can lead to large monetary benefits. Cell-by-cell samples of the froth products, selected feed and pulp-products were analyzed for flow rate, particle size distribution and chemical composition. In addition, certain samples were analyzed on an assay-by-size basis and hydrodynamic measurements of certain flotation cells were also performed. This detailed experimental dataset was then used to calibrate a floatability component model of the process. Longer mainline residence time resulted in significant Fe₂O₃ losses while yielding little benefit in terms of SiO₂ product grade. Scavenger 2 has twice the residence time of scavenger 1 while having to treat only 10% of the SiO₂, resulting in high Fe₂O₃ recoveries to the froth and poor separation. In addition, it is shown that the Fe₂O₃ exhibits true flotation behavior resulting in increased Fe₂O₃ losses. Simulations using the floatability component model identified avenues of process improvement to address the identified behavior. The insight provided by the simulations into the dynamics of the flotation process is invaluable for process engineers.