QEMSCAN® simulation improvement in complex microporous material: A case study on nickel laterites, A. Kanzari, A. Back, S.B. Blancher, T. Wallmach, V. Delarue, M. Pierre, and T. Bui

This study investigates nickel laterite porosity and its impact on geometallurgical modelling. We performed a detailed mineralogical study on a set of Ni-laterite samples originating from New Caledonia. Samples were analysed after heavy liquid separation by automated SEM (QEMSCAN®). Laboratory and QEMSCAN® modelling results do not correlate well. Ideal physical mineral properties such as mineral densities used in QEMSCAN® modelling do not match the actual physical densities of minerals. The presence of empty or water-filled pores can represent important amounts in minerals and change the overall density of a particle. An adequate modelling of real density separation of minerals and particles should consider the water-filled pores. The first step towards a better simulation was to integrate pores larger than 10 μm in diameter filled with water (density of 1 g/cm3) in the QEMSCAN® database. The results approached, but did not match the physical laboratory results. The second step was to identify nanometer to micrometer sized pores, which could not be identified by QEMSCAN®. It was possible to identify nanometric pores in enstatite, Fe-oxides, serpentine, talc and quartz, and radial aggregates of fibrous Fe-oxyhydroxides with high resolution SEM (HR-SEM). The quantification of those micro-sized pores was performed on 2D HR-SEM images using image-processing techniques developed in Python to calculate the nanometer to micrometer scale pores in each mineral. Despite the limitation of converting 2D imaging into 3D objects, in our case pore sizes and porosities, we were able to calculate a mean value of porosity for each mineral. The integration of this porosity in the calculation of particle densities yielded a good match between the QEMSCAN® model and the laboratory experiment results. Despite the complexity of a strongly weathered material, such as Ni-laterites, a good knowledge of the ore makes it possible to build reliable processing models. Keywords: Mineralogy, geometallurgy, nickel laterites, SEM, QEMSCAN®, porosity, density