422 XXXI International Mineral Processing Congress 2024 Proceedings/Washington, DC/Sep 29–Oct 3
for high sorting capacity. This is done by special program
architecture and algorithm solutions allowing efficient
management of the calculation routines and sorting priori-
ties (Kolacz, 2014). This allows achieving still high separa-
tion capacity and good efficiency, where the product purity
can reach even 95–99% level (Kolacz, 2014). Such results
can be achieved in different production scales starting from
few up to several hundred t/h, depending on the process
requirements.
Main Sensor Types
The output signals from different sensors, provide various
information about the processed material. Figure 2, shows
the XRT image of the Cu/Au ore after processing using
standard XRT-DE (Dual Energy) sensor technology. In
this case, the transmitted X-ray photons are analyzed with
standard spatial resolution (typically 0.4–0.8 mm) and
their energy is measured by X-ray sensors. X-ray attenu-
ation depends on the atomic number of the material and
this effect is used to recognize different material groups. As
shown in Figure 2, the result image is presented in form of
separate colors, where in this case the blue color shows low
density materials and the yellow and green colors present
high-density particles. Each particle is described by a frac-
tion of the defined color content within its contours, thus
providing an information about the content of the specified
atomic number group in such particles. This information
can further be related to the ore content inside the particle.
By applying a threshold and comparing it with the calcu-
lated ore content, such particles are classified by the soft-
ware to the rejected or non-rejected fractions.
The similar sensing technology can be used with high
spatial resolution XRT sensors (Kolacz, 2019). These sen-
sors are based on high resolution matrix analyzing the X-ray
photons with 50 microns spatial resolution (Polansky et al.
2018). The example image from such sensors is given in
Figure 3, where dispersed fine Au particles are identified.
The characteristics of the X-ray attenuation for different
energy levels of the photons (shown on the right side), pro-
vides a possibility to differentiate some types of materials.
Such materials are impossible to recognize with the stan-
dard sensors, due to their fine sizes and the limited analysis
within two energy levels (dual-energy sensors), rather than
full energy spectrum. This brings possibilities for better
identification and sorting of highly disseminated ores like
copper or gold ore.
Figure 4 shows the response image from the optical
camera, where the analyzed colors are recognized and pre-
sented in geometrical forms on the particle surface (right
side of the image). The surface analysis can be shown for
different color frequencies, intensities, and saturation. This
gives possibilities to identify details, which are hardly vis-
ible in the image, and classify them into different color
classes. Depending on the content of each type of color
class, when compared to the particle contours, it is pos-
sible to describe such particles by these characteristic values.
Figure 2. Standard XRT sensor response indicating product intrusions (yellow/green) in waste rock material (blue)
for high sorting capacity. This is done by special program
architecture and algorithm solutions allowing efficient
management of the calculation routines and sorting priori-
ties (Kolacz, 2014). This allows achieving still high separa-
tion capacity and good efficiency, where the product purity
can reach even 95–99% level (Kolacz, 2014). Such results
can be achieved in different production scales starting from
few up to several hundred t/h, depending on the process
requirements.
Main Sensor Types
The output signals from different sensors, provide various
information about the processed material. Figure 2, shows
the XRT image of the Cu/Au ore after processing using
standard XRT-DE (Dual Energy) sensor technology. In
this case, the transmitted X-ray photons are analyzed with
standard spatial resolution (typically 0.4–0.8 mm) and
their energy is measured by X-ray sensors. X-ray attenu-
ation depends on the atomic number of the material and
this effect is used to recognize different material groups. As
shown in Figure 2, the result image is presented in form of
separate colors, where in this case the blue color shows low
density materials and the yellow and green colors present
high-density particles. Each particle is described by a frac-
tion of the defined color content within its contours, thus
providing an information about the content of the specified
atomic number group in such particles. This information
can further be related to the ore content inside the particle.
By applying a threshold and comparing it with the calcu-
lated ore content, such particles are classified by the soft-
ware to the rejected or non-rejected fractions.
The similar sensing technology can be used with high
spatial resolution XRT sensors (Kolacz, 2019). These sen-
sors are based on high resolution matrix analyzing the X-ray
photons with 50 microns spatial resolution (Polansky et al.
2018). The example image from such sensors is given in
Figure 3, where dispersed fine Au particles are identified.
The characteristics of the X-ray attenuation for different
energy levels of the photons (shown on the right side), pro-
vides a possibility to differentiate some types of materials.
Such materials are impossible to recognize with the stan-
dard sensors, due to their fine sizes and the limited analysis
within two energy levels (dual-energy sensors), rather than
full energy spectrum. This brings possibilities for better
identification and sorting of highly disseminated ores like
copper or gold ore.
Figure 4 shows the response image from the optical
camera, where the analyzed colors are recognized and pre-
sented in geometrical forms on the particle surface (right
side of the image). The surface analysis can be shown for
different color frequencies, intensities, and saturation. This
gives possibilities to identify details, which are hardly vis-
ible in the image, and classify them into different color
classes. Depending on the content of each type of color
class, when compared to the particle contours, it is pos-
sible to describe such particles by these characteristic values.
Figure 2. Standard XRT sensor response indicating product intrusions (yellow/green) in waste rock material (blue)