Novel techniques in ore characterisation and sorting

Abstract

Ore sorting is becoming increasingly important to the minerals industry but is a difficult technology to apply effectively to many ore types. Microwave excitation and infrared detection (MW/IR) is a potential new sensor technology for sorting copper sulphide ores.This technology utilises the high dielectric permittivity of copper sulphide minerals and relatively low dielectric properties of gangue minerals to selectively heat copper rich particles and thereby gauge particle grade from temperature. To date, the dielectric properties of minerals and the role of mineral structures within particles are not fully understood and can vary significantly from deposit to deposit due to differences in geochemistry and genesis.These variations in mineralogy and dielectric properties can mean that some ores are unsuitable for sorting. The aim of this thesis is to develop a characterisation methodology to assess the suitability of ores for MW/IR based sorting from small sets of particles.The microwave heating of six different ores, chosen to represent a range of minerals and mineral textures, was characterised using randomly split particles, a domestic microwave oven and thermal imaging. The dielectric properties of core and powder subsamples of particles from four ores were then measured using a resonant cavity perturbation experiments at The University of Nottingham. Three effects were identified that may influence the observed relationship between microwave heating and dielectric properties: strong interaction with the magnetic field, mineral structures and sulphide heterogeneity in samples. These effects were investigated using radiofrequency magnetic permeability measurements, rotation of rock cylinders in a rectangular waveguide resonant cavity and automated scanning electron microscopy (MLA). The modal mineralogy of ore samples was measured using MLA while geochemistry was investigated using electron probe microscopy. The dielectric properties of mineral groups were then calculated using systems of linear equations based on the LandauLifschitz-Looyenga mixture equation as well as modal mineralogy and dielectric property data. Finally, numerical simulations of microwave heating were performed using data on mineralogy, mineral dielectric properties and variability in particle heating to determine to assess the potential of rocks types for sorting.ii MW/IR characterisation showed that quartzite, monzonite and skarn ores have distinct particle temperature distributions and that variation in the pixel temperatures of individual particle surfaces might be useful in distinguishing particle petrology. A power law was found to describe the relationship between microwave heating and dielectric loss tangent (R 2 = 79%) although a good fit could be achieved with a linear relationship (R 2 = 89%) when specific, termed anomalous, particles were excluded. Magnetic permeability measurements indicated that there was no significant difference in permeability between normal and anomalously heating particles. Changes in cavity per...

Understanding microwave induced sorting of porphyry copper ores

“…GHz, as indicated previously by the dielectric properties of minerals measured by Genn (2013) and would thus provide a suitable representation for the chalcopyrite used in the FLAC models with respect to individual grain heating and thermal profile development. …”

Microwave processing of minerals

“…The values in Table 4.8 were obtained from cavity perturbation measurements (more details can be found in section 2.4.1), using the "C1" cavity at University of Nottingham, UK; this was done by (Genn, 2012). The measurements were done on powder samples; the powder samples were made from 15 ore particles (belonging to DOME ore: JK2490-1), which were chosen based on their heating performances.…”

“…The measurements were done on powder samples; the powder samples were made from 15 ore particles (belonging to DOME ore: JK2490-1), which were chosen based on their heating performances. (Genn, 2012) According to Figure 4.19, the particles from Table 4.8 can be divided into 3 groups based on their loss tangent; first group consist of particles with (particles 1-6), in second group would be particles with (particles 7-13) and third group would be reserved for particles with (particles 14 and 15). If these particles are exposed to the same MW field, their temperature could be also divided into these three groups (first group would be particles with the coolest temperature and third group would consist of the hottest particles).…”

“…It was assumed that all particles have the same density and specific heat capacity. Table 4.10 Parameters used in estimation of temperature increase of Dome ore particles measured by (Genn, 2012) Figure 4.21 represents the average temperature increase for each particle across the belt after heating inside the pentagonal cavity. Few types of particle are not present within certain paths (for example, there is no particle 14 in the 6 th path) and that is the reason why temperatures of some particles are not presented in Figure 4.21.…”

“…The loss tangents for these particles are presented in Table 5.4; dielectric constant and dielectric loss factor are presented in Table 4.8. According to Figure 5.6, the particles from Table 5.4 can be divided into 3 groups based on their loss tangents; the dielectric properties are measured by (Genn, 2012 Table 5.4 Loss tangent for various shape/size particles used simulations; particles are marked with numbers with dielectric constant and dielectric loss for particles presented in Table 4.8…”