906 XXXI International Mineral Processing Congress 2024 Proceedings/Washington, DC/Sep 29–Oct 3
truth. The mineral and elemental grade of the tested sam-
ples was calculated knowing the tail and concentrate por-
tions and their ICP-MS results in the sample mixture. The
same analysis was done to calculate the PSDs from manual
sieving of the concentrate and the tail samples in a metal-
lurgy laboratory.
Modal Mineralogy—Learning and Training
The MinDet DL algorithm is trained to estimate the
modal mineralogy of particles in slurries. To train the
Deep Learning algorithm, a dataset of labelled particles by
their mineralogy needs to be collected for possible min-
erals encountered in the slurry sample. Several samples
were pulverized and analysed with XRD to determine the
mineral composition present in this ore, which the bulk
sulfides were Chalcopyrite, Pyrite, Galena, Sphalerite,
with traces of Bornite. A representative sample of each
mineral was obtained and imaged under the Scanning
Electron Microscopy (SEM) and Energy Dispersive X-Ray
Spectroscopy (EDS or EDX). The instrument used in
this study was the Hitachi TM3030 Scanning Electron
Microscopy (SEM) (Nishimura et al., 2016). The analy-
ses provide the ground truth mineralogy necessary to label
the MinDet microscopy image of any particle. The SEM
images provide a grayscale image where contrast (different
atomic number) and texture can be used to identify regions
of different minerals. Then, the EDS uses a focused beam
of X-Rays at any small point which excites electrons and
measures the X-ray emissions which are characteristic to the
known elements. However, EDS is generally classed as a
semi-quantitative analysis method as the accuracy depends
on the calibration of the machine on known samples and
sometimes incorrect automatic element identification
(Newbury et al., 1995 Newbury, 2009 Newbury et al.,
2013 Newbury et al., 2007).
Table 2 shows several examples of fully liberated miner-
als, their images, and their scans used to develop the min-
eral detection algorithm. These minerals are fully liberated
because the SEM images do not show grain boundaries and
have homogenous texture. Galena, PbS (86.6% Pb, 13.4%
S) is a light gray and silvery mineral known to form cubic
crystal structures which can also be observed in the par-
ticles imaged with right angled corners. The EDS scan also
shows about 90% Pb and around 5% S which is close to
the elemental composition of Galena. There are also traces
of oxygen which could be due to surface oxidation of the
particles.
Pure Sphalerite, ZnS (67.1% Zn, 32.9% S) can form
red colour shades where increasing Fe content forms darker
crystals. In these examples, the EDS scan shows about
37–53% Zn content with 4–9% Fe and 20–28% S indicat-
ing that it is likely Sphalerite. There are also traces of oxygen
for surface oxidation. There was also a significant number
of other elements which could either be contamination or
incorrect elemental identification. Chalcopyrite, CuFeS2
(34.5% Cu, 30.5% Fe, 35.0% S) is a brassy yellow colour
Figure 5. A screenshot of the MinDet machine learning software and user interface under operation
truth. The mineral and elemental grade of the tested sam-
ples was calculated knowing the tail and concentrate por-
tions and their ICP-MS results in the sample mixture. The
same analysis was done to calculate the PSDs from manual
sieving of the concentrate and the tail samples in a metal-
lurgy laboratory.
Modal Mineralogy—Learning and Training
The MinDet DL algorithm is trained to estimate the
modal mineralogy of particles in slurries. To train the
Deep Learning algorithm, a dataset of labelled particles by
their mineralogy needs to be collected for possible min-
erals encountered in the slurry sample. Several samples
were pulverized and analysed with XRD to determine the
mineral composition present in this ore, which the bulk
sulfides were Chalcopyrite, Pyrite, Galena, Sphalerite,
with traces of Bornite. A representative sample of each
mineral was obtained and imaged under the Scanning
Electron Microscopy (SEM) and Energy Dispersive X-Ray
Spectroscopy (EDS or EDX). The instrument used in
this study was the Hitachi TM3030 Scanning Electron
Microscopy (SEM) (Nishimura et al., 2016). The analy-
ses provide the ground truth mineralogy necessary to label
the MinDet microscopy image of any particle. The SEM
images provide a grayscale image where contrast (different
atomic number) and texture can be used to identify regions
of different minerals. Then, the EDS uses a focused beam
of X-Rays at any small point which excites electrons and
measures the X-ray emissions which are characteristic to the
known elements. However, EDS is generally classed as a
semi-quantitative analysis method as the accuracy depends
on the calibration of the machine on known samples and
sometimes incorrect automatic element identification
(Newbury et al., 1995 Newbury, 2009 Newbury et al.,
2013 Newbury et al., 2007).
Table 2 shows several examples of fully liberated miner-
als, their images, and their scans used to develop the min-
eral detection algorithm. These minerals are fully liberated
because the SEM images do not show grain boundaries and
have homogenous texture. Galena, PbS (86.6% Pb, 13.4%
S) is a light gray and silvery mineral known to form cubic
crystal structures which can also be observed in the par-
ticles imaged with right angled corners. The EDS scan also
shows about 90% Pb and around 5% S which is close to
the elemental composition of Galena. There are also traces
of oxygen which could be due to surface oxidation of the
particles.
Pure Sphalerite, ZnS (67.1% Zn, 32.9% S) can form
red colour shades where increasing Fe content forms darker
crystals. In these examples, the EDS scan shows about
37–53% Zn content with 4–9% Fe and 20–28% S indicat-
ing that it is likely Sphalerite. There are also traces of oxygen
for surface oxidation. There was also a significant number
of other elements which could either be contamination or
incorrect elemental identification. Chalcopyrite, CuFeS2
(34.5% Cu, 30.5% Fe, 35.0% S) is a brassy yellow colour
Figure 5. A screenshot of the MinDet machine learning software and user interface under operation