XXXI International Mineral Processing Congress 2024 Proceedings/Washington, DC/Sep 29–Oct 3 903
in a flotation process (Amini et al., 2009 Mu and Peng,
2023 Liu et al., 2023 Zhang 2023).
Several methodologies have been developed and
adapted to detect and determine the composition of miner-
als since 1669. A summary of the chronological progress
of mineral detection methodologies is shown in Figure 3
(Wright, 1942 Grey, 2022).
In the early days, contact and reflecting goniometers
were invented to observe and study mineral crystals and
their uniform geometrical shapes using telescopes. At the
end of the 19th century, X-ray machines were invented
which was also used in mineralogy studies. The solid-state
X-ray detector or energy-dispersive spectrometer (EDS)
was developed in the late 1960s and rapidly found use
as electron-beam instruments (Fitzgerald et al., 1968)
because of its speed in collecting and simultaneously dis-
playing x-ray data from a wide energy range (Williams et
al., 1986). Scanning Electron Microscopes (SEM) with
energy dispersive X-ray (EDS) was also wildly deployed in
80s and 90s and still in use until today. Historically, the
mineral detection process was a qualitative method due to
the nature of the measurement methods mentioned here.
Therefore, qualitative methods known as modal mineralogy
such as QEMSCAN and Mineralogy Liberation Analysis
(MLA), and more recently automated solutions like ZEISS
Mineralogic and TESCAN Integrated Mineral Analyzer
(TIMA) were developed in the last few decades.
However, the measurement techniques mentioned
above require special samples to be prepared, such as pol-
ished sections, resulting in relatively lengthy turnaround
time between sampling and analysis results. Because of this,
they cannot be implemented as an online measurement
method in their current format. Table 1 shows the time
required to measure key sample attributes after collecting
a sample. Drying and preparation of a representative sub
sample is not included in the timing.
Among these methodologies, only elemental analys-
ers like X-Ray Fluorescence (XRF) (Holynska et al., 1985
Watt, 1983) and Laser Induced Breakdown Spectroscopy
(LIBS) (Khajehzadeh et al., 2016 Barrette &Turmel, 2001)
are suitable for online process monitoring and control.
These methodologies provide an estimation of bulk mineral
distribution through a mass balance of the elemental mass
measurement through calibration of known composites.
However, the elemental analysis lacks mineral association
and liberation degree which are also crucial information in
flotation performance. Furthermore, the online XRF has
poor accuracies for lighter elements such as Ca and S which
makes it difficult to identify clay minerals and differentiate
the copper sulfide minerals from each other (Kewe et al.,
2014 Amar et al., 2022). In contrast, LIBS does not have
this limitation but is less accurate for heavier elements like
copper (Al-Eshaikh et al., 2016).
An emerging area of research is to utilise optical spec-
troscopy for modal mineralogy. The earliest known study
was by Haavisto et al. (2008) to predict the Zn, Cu, S, Fe,
and Pb elemental content using visible and near-infrared
(VNIR) spectroscopy. Another approach utilising reflec-
tive optical spectroscopy and chemometrics was done by
Kewe et al. (2014) to measure sulfur, sulfides, gold, and
%passing 38 μm but did not disclose the statistical model
nor the range of wavelength spectrum used. Khajehzadeh
et al. (2017) combined VNIR optical spectroscopy, XRF,
and LIBS together to measure quartz, magnetite, hematite,
Table 1. Sample turnaround time to collect, prepare, and
obtain the result for key attributes
Information
Example
Methodology
Minimum Time
Required
Modal Mineralogy MLA 4 shifts
QEMSCAN 2 shifts
XRD 2 shifts
Elemental Grade XRF 1.5 min
LIBS 1.5 min
ICP, Titration,
Fire Assay
2 shifts
SEM +EDS 2 shifts
Liberation MLA 4 shifts
QEMSCAN 2 shifts
Figure 3. History of mineral detection and composition analysis (Wright, 1942 Grey, 2022)
in a flotation process (Amini et al., 2009 Mu and Peng,
2023 Liu et al., 2023 Zhang 2023).
Several methodologies have been developed and
adapted to detect and determine the composition of miner-
als since 1669. A summary of the chronological progress
of mineral detection methodologies is shown in Figure 3
(Wright, 1942 Grey, 2022).
In the early days, contact and reflecting goniometers
were invented to observe and study mineral crystals and
their uniform geometrical shapes using telescopes. At the
end of the 19th century, X-ray machines were invented
which was also used in mineralogy studies. The solid-state
X-ray detector or energy-dispersive spectrometer (EDS)
was developed in the late 1960s and rapidly found use
as electron-beam instruments (Fitzgerald et al., 1968)
because of its speed in collecting and simultaneously dis-
playing x-ray data from a wide energy range (Williams et
al., 1986). Scanning Electron Microscopes (SEM) with
energy dispersive X-ray (EDS) was also wildly deployed in
80s and 90s and still in use until today. Historically, the
mineral detection process was a qualitative method due to
the nature of the measurement methods mentioned here.
Therefore, qualitative methods known as modal mineralogy
such as QEMSCAN and Mineralogy Liberation Analysis
(MLA), and more recently automated solutions like ZEISS
Mineralogic and TESCAN Integrated Mineral Analyzer
(TIMA) were developed in the last few decades.
However, the measurement techniques mentioned
above require special samples to be prepared, such as pol-
ished sections, resulting in relatively lengthy turnaround
time between sampling and analysis results. Because of this,
they cannot be implemented as an online measurement
method in their current format. Table 1 shows the time
required to measure key sample attributes after collecting
a sample. Drying and preparation of a representative sub
sample is not included in the timing.
Among these methodologies, only elemental analys-
ers like X-Ray Fluorescence (XRF) (Holynska et al., 1985
Watt, 1983) and Laser Induced Breakdown Spectroscopy
(LIBS) (Khajehzadeh et al., 2016 Barrette &Turmel, 2001)
are suitable for online process monitoring and control.
These methodologies provide an estimation of bulk mineral
distribution through a mass balance of the elemental mass
measurement through calibration of known composites.
However, the elemental analysis lacks mineral association
and liberation degree which are also crucial information in
flotation performance. Furthermore, the online XRF has
poor accuracies for lighter elements such as Ca and S which
makes it difficult to identify clay minerals and differentiate
the copper sulfide minerals from each other (Kewe et al.,
2014 Amar et al., 2022). In contrast, LIBS does not have
this limitation but is less accurate for heavier elements like
copper (Al-Eshaikh et al., 2016).
An emerging area of research is to utilise optical spec-
troscopy for modal mineralogy. The earliest known study
was by Haavisto et al. (2008) to predict the Zn, Cu, S, Fe,
and Pb elemental content using visible and near-infrared
(VNIR) spectroscopy. Another approach utilising reflec-
tive optical spectroscopy and chemometrics was done by
Kewe et al. (2014) to measure sulfur, sulfides, gold, and
%passing 38 μm but did not disclose the statistical model
nor the range of wavelength spectrum used. Khajehzadeh
et al. (2017) combined VNIR optical spectroscopy, XRF,
and LIBS together to measure quartz, magnetite, hematite,
Table 1. Sample turnaround time to collect, prepare, and
obtain the result for key attributes
Information
Example
Methodology
Minimum Time
Required
Modal Mineralogy MLA 4 shifts
QEMSCAN 2 shifts
XRD 2 shifts
Elemental Grade XRF 1.5 min
LIBS 1.5 min
ICP, Titration,
Fire Assay
2 shifts
SEM +EDS 2 shifts
Liberation MLA 4 shifts
QEMSCAN 2 shifts
Figure 3. History of mineral detection and composition analysis (Wright, 1942 Grey, 2022)