Access to Reserved content is restricted to users who have purchased access to this material.
Abstract
Discussion Forum (0)
Ore sorting has experienced a resurgence in popularity recently as high-grade resources are depleted and energy costs continue to rise. Modern ore sorting technology employs sensors, such as X-ray transmission (XRT), X-ray fluorescence (XRF), and conductivity sensors to detect differences between ore particles. However, each of these sensor types has different limitations when applied in ore sorting, such as the limited penetration depth (roughly 100 mm) of XRT and the low throughput of XRF sorting. In this study, a new sensing method employing microwave imaging (MWI) is proposed to distinguish between rocks that contain valuable minerals or metals from those that do not. Compared to more established sensors, the MWI sensor has the potential to penetrate deeper into the rock particles and be used as a supplementary method aiming at those ores with high dielectric constant and conductivity contrast between valuable minerals and gangue. This study aims at establishing if the MWI is feasible to be a sensor regarding to the ore sorting application and exploring its potential value in the specific sensor-based ore sorting field. Preliminary investigations have validated that the MWI method can successfully detect the position of metal inclusions (>3 mm) in a homogeneous core model and the presence of metal inclusions (>0.3 mm) in a heterogeneous core model using a confocal microwave imaging (CMI) algorithm.
Ore sorting has experienced a resurgence in popularity recently as high-grade resources are depleted and energy costs continue to rise. Modern ore sorting technology employs sensors, such as X-ray transmission (XRT), X-ray fluorescence (XRF), and conductivity sensors to detect differences between ore particles. However, each of these sensor types has different limitations when applied in ore sorting, such as the limited penetration depth (roughly 100 mm) of XRT and the low throughput of XRF sorting. In this study, a new sensing method employing microwave imaging (MWI) is proposed to distinguish between rocks that contain valuable minerals or metals from those that do not. Compared to more established sensors, the MWI sensor has the potential to penetrate deeper into the rock particles and be used as a supplementary method aiming at those ores with high dielectric constant and conductivity contrast between valuable minerals and gangue. This study aims at establishing if the MWI is feasible to be a sensor regarding to the ore sorting application and exploring its potential value in the specific sensor-based ore sorting field. Preliminary investigations have validated that the MWI method can successfully detect the position of metal inclusions (>3 mm) in a homogeneous core model and the presence of metal inclusions (>0.3 mm) in a heterogeneous core model using a confocal microwave imaging (CMI) algorithm.
Design of Microwave Imaging Systems for Sensor-based Ore Sorting
Beichen Duan
Abstract
Discussion Forum (0)
Ore sorting has experienced a resurgence in popularity recently as high-grade resources are depleted and energy costs continue to rise. Modern ore sorting technology employs sensors, such as X-ray transmission (XRT), X-ray fluorescence (XRF), and conductivity sensors to detect differences between ore particles. However, each of these sensor types has different limitations when applied in ore sorting, such as the limited penetration depth (roughly 100 mm) of XRT and the low throughput of XRF sorting. In this study, a new sensing method employing microwave imaging (MWI) is proposed to distinguish between rocks that contain valuable minerals or metals from those that do not. Compared to more established sensors, the MWI sensor has the potential to penetrate deeper into the rock particles and be used as a supplementary method aiming at those ores with high dielectric constant and conductivity contrast between valuable minerals and gangue. This study aims at establishing if the MWI is feasible to be a sensor regarding to the ore sorting application and exploring its potential value in the specific sensor-based ore sorting field. Preliminary investigations have validated that the MWI method can successfully detect the position of metal inclusions (>3 mm) in a homogeneous core model and the presence of metal inclusions (>0.3 mm) in a heterogeneous core model using a confocal microwave imaging (CMI) algorithm.
Ore sorting has experienced a resurgence in popularity recently as high-grade resources are depleted and energy costs continue to rise. Modern ore sorting technology employs sensors, such as X-ray transmission (XRT), X-ray fluorescence (XRF), and conductivity sensors to detect differences between ore particles. However, each of these sensor types has different limitations when applied in ore sorting, such as the limited penetration depth (roughly 100 mm) of XRT and the low throughput of XRF sorting. In this study, a new sensing method employing microwave imaging (MWI) is proposed to distinguish between rocks that contain valuable minerals or metals from those that do not. Compared to more established sensors, the MWI sensor has the potential to penetrate deeper into the rock particles and be used as a supplementary method aiming at those ores with high dielectric constant and conductivity contrast between valuable minerals and gangue. This study aims at establishing if the MWI is feasible to be a sensor regarding to the ore sorting application and exploring its potential value in the specific sensor-based ore sorting field. Preliminary investigations have validated that the MWI method can successfully detect the position of metal inclusions (>3 mm) in a homogeneous core model and the presence of metal inclusions (>0.3 mm) in a heterogeneous core model using a confocal microwave imaging (CMI) algorithm.
{{ help_message }}
{{filter}}