XXXI International Mineral Processing Congress 2024 Proceedings Page 872 (896 of 3836)

902 XXXI International Mineral Processing Congress 2024 Proceedings/Washington, DC/Sep 29–Oct 3
This study indicates that even within major copper miner-
als in a concentrator, process optimisation could result in
enhanced metal yield in an operation.
Welsby et al. (2010) showed that galena particles in the
middle size fractions which are fully liberated float faster
than particles in the coarser or finer size fractions. Flotation
kinetics was even slower for the galena particles which has
association with gangue minerals. Several studies have also
demonstrated that gangue minerals such as clays and pyrite
impact the flotation performance (Farrokhpay et al., 2016
Jeldres et al., 2019 Jefferson, 2023 Wang et al., 2024).
Identifying those minerals and their grades determines the
optimal reagent suite (depressant or dispersant type and
dosage) for maximising recovery and separation efficiency
Figure 1. Influential parameters on flotation process (after Klimpel 1995)
Figure 2. Impact of NaHS dosage and pH level on Chalcocite, Bornite and chalcopyrite flotation recovery
(after Matsuoka et al., 2020)

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902 XXXI International Mineral Processing Congress 2024 Proceedings/Washington, DC/Sep 29–Oct 3
This study indicates that even within major copper miner-
als in a concentrator, process optimisation could result in
enhanced metal yield in an operation.
Welsby et al. (2010) showed that galena particles in the
middle size fractions which are fully liberated float faster
than particles in the coarser or finer size fractions. Flotation
kinetics was even slower for the galena particles which has
association with gangue minerals. Several studies have also
demonstrated that gangue minerals such as clays and pyrite
impact the flotation performance (Farrokhpay et al., 2016
Jeldres et al., 2019 Jefferson, 2023 Wang et al., 2024).
Identifying those minerals and their grades determines the
optimal reagent suite (depressant or dispersant type and
dosage) for maximising recovery and separation efficiency
Figure 1. Influential parameters on flotation process (after Klimpel 1995)
Figure 2. Impact of NaHS dosage and pH level on Chalcocite, Bornite and chalcopyrite flotation recovery
(after Matsuoka et al., 2020)

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901
Development and Trial of an Online Modal Mineralogy Module
for Process Plant Slurries
Eiman Amini, Edwin Koh, Nick Beaton
Orica Digital Solutions, Australia
ABSTRACT: A module was developed using a Machine Learning (ML) algorithm and a sampling device to
estimate the modal mineralogy of a slurry stream in real-time. The prototype automatically prepares the samples
taken from a slurry stream, such as a conventional flotation feed, and routes them to the measurement chambers.
The ML algorithm continuously analyses samples from the slurry stream to report mineralogy by size, particle
size distribution, and percent solids in that stream. The module was tested with an ore sample taken from a
copper operation in Australia. The outcomes generated by the device during this trial were then compared with
the results of chemical assays conducted on the representative samples taken before the trial. There was a strong
correlation between the assay results and mineral compositions reported by the module.
Keywords: Mineral detection, Online measurement, Process improvement, Machine Vision
INTRODUCTION
Mineral separation and concentration usually occur after
an ore is processed through a grinding circuit and miner-
als are almost liberated. There are several parameters which
impact the separation efficiency of the process. One of the
most used separation processes in the industry for base and
precious metals is froth flotation. In froth flotation, there
are many parameters which affect the process performance
and can be grouped into chemistry, operation, and equip-
ment according to Klimpel (1995) as shown in Figure 1.
Studies investigating the effect of parameters like ore physi-
cal properties, pulp chemistry, and process hydrodynamics
can be found in literature (Berglund, 1991 Ralston, 1991
Kelebek et al., 1995 Mathe et al., 2000 Amini et al.,
2016a Amini et al., 2016b Amini et al., 2017 Shahbazi et
al., 2017 Amankwaa-Kyeremeh et al., 2021 Forson et al.,
2022 Amankwaa-Kyeremeh et al., 2023).
Equipment and chemistry components are selected or
controlled according to the operations conditions such as
feed flow rate, mineralogy, particle size distribution, and
density in a separation process. Process operators typically
adjust operating conditions like the reagent type and dos-
age, air flow rate, and pulp level to achieve the highest
metal production at the target grade. Because of this, min-
eralogy plays the most important role as it defines the pulp
chemistry, liberation size, and residence time of the process.
Mineralogy of an ore includes, valuable and gangue mineral
distribution, minerals liberation degree, mineral associa-
tion, and mineral grain size which all directly impact the flo-
tation performance. Other influential components like pH,
feed rate, feed size distribution, pulp density, air flowrate,
and pulp chemistry can be measured and controlled online.
However, choosing appropriate set points for those param-
eters to maximise separation and recovery strongly depends
on the feed ore mineralogy. For instance, Matsuoka et al.
(2020) investigated the impact of pulp chemistry on three
major industrial copper minerals (Chalcocite, Bornite, and
Chalcopyrite) flotation kinetics and recovery (Figure 2).

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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)

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