904 XXXI International Mineral Processing Congress 2024 Proceedings/Washington, DC/Sep 29–Oct 3
and ferrorichterite. Leroy et al. (2011) demonstrated that it
was possible to use optical-digital microscopy to determine
degree of liberation and particle size distribution. Another
study by Leroy &Pirard (2019) proposed a methodology
for single-particle mineral recognition through multi-spec-
tral microscopy images and machine learning to identify
pyrite, chalcopyrite, arsenopyrite, sphalerite, and galena.
However, isolating and evaluating single-particle images in
pulp to collect a representative sample to characterise pro-
cess streams would be infeasible due to time limitations.
Furthermore, the methodology does not evaluate grain size
which is necessary for calculating grade by size. Finally,
Koh et al. (2024) demonstrated a methodology using deep
learning and optical microscopy to estimate modal miner-
alogy of a slurry sample with high accuracy. However, the
study did not consider the aspects of sampling and sample
preparation required for a commercial online analyser.
Therefore, the aim of this study is to design and
develop a sampling and measurement apparatus capable
of conducting modal mineralogy analysis on a sample col-
lected from a slurry stream or tank in an operating pilot
and full-scale plant. The apparatus has overcome the limita-
tions mentioned above to speed up the modal mineralogy
analysis process and reduces the complexity of preparation
and sampling of slurries which is call online mineral detec-
tion apparatus or in short (MinDet). The agility of MinDet
and reliability of the results is validated to show its compe-
tence and application in optimising recovery and yield of
separation processes in real time.
METHODOLOGY
In MinDet, there were three integrated components devel-
oped to estimate the modal mineralogy of slurry streams
in real time. They are: sample collection and preparation
unit, optical microscopy scanning, and modal mineralogy
software for sample analysis. The methodology was devel-
oped and validated with a sulfide polymetallic ore sample
from a poly-metal mine in New South Wales, Australia.
The samples were processed in a laboratory scale flotation
cell to generate high-grade concentrate and low-grade tail.
Then, flotation samples were blended with pre-calculated
ratios of concentrate and tailings with three particle size
distributions to create a range of possible samples in the
concentrator.
To collect a dataset for the mineral recognition software,
random samples of the homogenised concentrate and tail-
ings were analysed with X-ray Powder Diffraction (XRPD)
and SEM +EDS to determine the ground truth mineralogy
and elemental constituents. The bulk occurring minerals in
the concentrate were Chalcopyrite (CuFeS2), Sphalerite
(ZnxFe(1-x)S), Galena (PbS), Pyrite (FeS2), Quartz (SiO2),
and Pennanite (Mn Al O
5
2+
3 10 8 ^AlSi h^OHh ).There
were also traces of Cubanite (CuFe2S3) and Felbertalite
(Cu2Pb6Bi8S19) which were neglected in the analysis. Once
the ground truth mineralogy was determined, a representa-
tive sample for each mineral were cross-referenced, identi-
fied, and labelled under the optical microscope.
Finally, to validate the modal mineralogy methodol-
ogy, tailing and concentrate samples were combined in
different portions from low grade (1.05% Chalcopyrite)
to 100% concentrate (51.1% Chalcopyrite) for calibration
and testing. The samples were sent for ICP-MS analysis to
determine the Cu, Fe, Pb, S, and Zn elemental contents.
MinDet identifies minerals in each image which was con-
verted to elemental assay using their mineral composition,
the surface of the image covered by the mineral, and the
mineral density. Then, the quantified results are compared
with the ICP-MS results which is quantitative as well.
Sample Collection and Preparation
Koh et al. (2024) demonstrated that a slurry sample such
as flotation feed and products requires separation of fine
particles (–38 μm) from the coarse particles (+38 μm) prior
to imaging with the optical microscope in-pulp. To achieve
this in continuous mode, a desliming cyclone replaces the
screening process. Then, both the underflow and overflow
of the cyclone flows through observation towers which con-
sists of a chamber with a viewing window. The sampling
unit and observation tower was designed and printed on a
3D printer to meet the unique requirements by the author.
The prototype can be run in batch and continuous modes,
where the continuous mode for use in the operation plant
and the batch mode/bench top version for use in metallur-
gical laboratories.
Optical Microscopy Scanning
In the middle of MinDet is a viewing chamber that exposes
the slurry sample for photography with a motorised optical
microscope. This is coupled with an adequate illumination
set up designed for scanning the particles near the surface of
the viewing chamber. Depending on the particle and min-
eral grain sizes, the adequate lens specification and digital
sensor resolution is selected for the application. For this ore
type, a 10× magnification lens and a high-definition (1920
× 1080-pixel at 472 µm × 266 µm) video at 30 frames/
second rate sensor was used. This analysis provides mineral
mix, elemental grade and stream particle size distribution
(PSD) in near real time for use in concentrator set point
decisions and control systems (Figure 4).
and ferrorichterite. Leroy et al. (2011) demonstrated that it
was possible to use optical-digital microscopy to determine
degree of liberation and particle size distribution. Another
study by Leroy &Pirard (2019) proposed a methodology
for single-particle mineral recognition through multi-spec-
tral microscopy images and machine learning to identify
pyrite, chalcopyrite, arsenopyrite, sphalerite, and galena.
However, isolating and evaluating single-particle images in
pulp to collect a representative sample to characterise pro-
cess streams would be infeasible due to time limitations.
Furthermore, the methodology does not evaluate grain size
which is necessary for calculating grade by size. Finally,
Koh et al. (2024) demonstrated a methodology using deep
learning and optical microscopy to estimate modal miner-
alogy of a slurry sample with high accuracy. However, the
study did not consider the aspects of sampling and sample
preparation required for a commercial online analyser.
Therefore, the aim of this study is to design and
develop a sampling and measurement apparatus capable
of conducting modal mineralogy analysis on a sample col-
lected from a slurry stream or tank in an operating pilot
and full-scale plant. The apparatus has overcome the limita-
tions mentioned above to speed up the modal mineralogy
analysis process and reduces the complexity of preparation
and sampling of slurries which is call online mineral detec-
tion apparatus or in short (MinDet). The agility of MinDet
and reliability of the results is validated to show its compe-
tence and application in optimising recovery and yield of
separation processes in real time.
METHODOLOGY
In MinDet, there were three integrated components devel-
oped to estimate the modal mineralogy of slurry streams
in real time. They are: sample collection and preparation
unit, optical microscopy scanning, and modal mineralogy
software for sample analysis. The methodology was devel-
oped and validated with a sulfide polymetallic ore sample
from a poly-metal mine in New South Wales, Australia.
The samples were processed in a laboratory scale flotation
cell to generate high-grade concentrate and low-grade tail.
Then, flotation samples were blended with pre-calculated
ratios of concentrate and tailings with three particle size
distributions to create a range of possible samples in the
concentrator.
To collect a dataset for the mineral recognition software,
random samples of the homogenised concentrate and tail-
ings were analysed with X-ray Powder Diffraction (XRPD)
and SEM +EDS to determine the ground truth mineralogy
and elemental constituents. The bulk occurring minerals in
the concentrate were Chalcopyrite (CuFeS2), Sphalerite
(ZnxFe(1-x)S), Galena (PbS), Pyrite (FeS2), Quartz (SiO2),
and Pennanite (Mn Al O
5
2+
3 10 8 ^AlSi h^OHh ).There
were also traces of Cubanite (CuFe2S3) and Felbertalite
(Cu2Pb6Bi8S19) which were neglected in the analysis. Once
the ground truth mineralogy was determined, a representa-
tive sample for each mineral were cross-referenced, identi-
fied, and labelled under the optical microscope.
Finally, to validate the modal mineralogy methodol-
ogy, tailing and concentrate samples were combined in
different portions from low grade (1.05% Chalcopyrite)
to 100% concentrate (51.1% Chalcopyrite) for calibration
and testing. The samples were sent for ICP-MS analysis to
determine the Cu, Fe, Pb, S, and Zn elemental contents.
MinDet identifies minerals in each image which was con-
verted to elemental assay using their mineral composition,
the surface of the image covered by the mineral, and the
mineral density. Then, the quantified results are compared
with the ICP-MS results which is quantitative as well.
Sample Collection and Preparation
Koh et al. (2024) demonstrated that a slurry sample such
as flotation feed and products requires separation of fine
particles (–38 μm) from the coarse particles (+38 μm) prior
to imaging with the optical microscope in-pulp. To achieve
this in continuous mode, a desliming cyclone replaces the
screening process. Then, both the underflow and overflow
of the cyclone flows through observation towers which con-
sists of a chamber with a viewing window. The sampling
unit and observation tower was designed and printed on a
3D printer to meet the unique requirements by the author.
The prototype can be run in batch and continuous modes,
where the continuous mode for use in the operation plant
and the batch mode/bench top version for use in metallur-
gical laboratories.
Optical Microscopy Scanning
In the middle of MinDet is a viewing chamber that exposes
the slurry sample for photography with a motorised optical
microscope. This is coupled with an adequate illumination
set up designed for scanning the particles near the surface of
the viewing chamber. Depending on the particle and min-
eral grain sizes, the adequate lens specification and digital
sensor resolution is selected for the application. For this ore
type, a 10× magnification lens and a high-definition (1920
× 1080-pixel at 472 µm × 266 µm) video at 30 frames/
second rate sensor was used. This analysis provides mineral
mix, elemental grade and stream particle size distribution
(PSD) in near real time for use in concentrator set point
decisions and control systems (Figure 4).