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).
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).