XXXI International Mineral Processing Congress 2024 Proceedings/Washington, DC/Sep 29–Oct 3 1119
very severe, with strong winds, snow and very harsh living
conditions.
For a given circuit in a mineral process plant, the opti-
mal throughput is usually limited by several constraints
such the ore hardness that can alter the particle size distri-
bution and the transport mechanisms in the mill due to the
complex rheology of the pulp made of varying degree of
fine particles and percent solids. The strategies to improve
the particle size distribution affects the grinding efficiency,
flotation and the water recovery during thickening and fil-
tration are very important for low grade ores. By operat-
ing closer to these constraints, considerable mill capacity is
gained. Flotation hydrodynamics and control technology
has matured considerably in the last decades in understand-
ing the interaction of the grinding circuit variations and
how these affect the froth flotation process performance.
The optimal tonnage processed is function of the min-
eral type, the mineralogical characteristics of the pulp,
rheology, the asset availability and the operating mode of
all process unit interacting in an industrial complex. The
objective is to find the optimal cut of grade at the mine
and the optimal cut size at the comminution to achieve
the best recovery grade combination to maximize profits.
At mills, energy costs have increased to liberate the metal-
bearing particles. The typical process control objective is
to maximize the metal production while lowering operat-
ing production and mining costs. Because grades at mines
are very low (less than 0.5% Copper), large volumes of ore
need to be process to achieve an economical production
target. While obtaining these targets, the amount of energy,
water and ore transport and the management of the very
large amount of waste has become extremely expensive.
Recent research (Dunne et al., 2019 Bascur 2019 Bascur
and Gorrie 2021, Concha and Bascur, 2024) document
new strategies of operational problems to reduce the hidden
operational losses, maximize copper recovery and produc-
tion and become more proactive, using grinding, flotation
and thickening predictive models to optimize the mineral
processing plant.
It is well known that in processing low grade ore it is
very important to reduce the variability in the grinding cir-
cuit to get the right grind cut point and a particle size distri-
bution in the mill that improves the water recovery rate in
the tailing thickeners. The metal production maximization
strategy is shown in Figure 4 with its left side exhibiting each
of the process units in operation to obtain the best results
in the rougher flotation recovery. On the right side, the fig-
ure shows the well know flotation elephant curve showing
that minerals in very small and large particles sizes do not
concentrate well in traditional flotation equipment (Lynch
et al., 1981 Trahar 1981, Bascur, Herbst and Freeh, 1986,
Bascur, 1991a). Figure 4 also shows the required reduction
of product size variability to be able to move closer to maxi-
mum net production targets in flotation. It is also known
that particles with a wide size distribution do not flocculate
well and are carried out with the overflow water in thicken-
ers. These examples make particle size distribution in the
flotation and thickener’s feed a major target.
Figure 5 shows how the OSB DIGIT unit operation
template is used to model the whole plant for production
variance improvement and coordination. This data model
enables us to identify the process constraints to satisfy the
Figure 3. Typical mineral processing plant
very severe, with strong winds, snow and very harsh living
conditions.
For a given circuit in a mineral process plant, the opti-
mal throughput is usually limited by several constraints
such the ore hardness that can alter the particle size distri-
bution and the transport mechanisms in the mill due to the
complex rheology of the pulp made of varying degree of
fine particles and percent solids. The strategies to improve
the particle size distribution affects the grinding efficiency,
flotation and the water recovery during thickening and fil-
tration are very important for low grade ores. By operat-
ing closer to these constraints, considerable mill capacity is
gained. Flotation hydrodynamics and control technology
has matured considerably in the last decades in understand-
ing the interaction of the grinding circuit variations and
how these affect the froth flotation process performance.
The optimal tonnage processed is function of the min-
eral type, the mineralogical characteristics of the pulp,
rheology, the asset availability and the operating mode of
all process unit interacting in an industrial complex. The
objective is to find the optimal cut of grade at the mine
and the optimal cut size at the comminution to achieve
the best recovery grade combination to maximize profits.
At mills, energy costs have increased to liberate the metal-
bearing particles. The typical process control objective is
to maximize the metal production while lowering operat-
ing production and mining costs. Because grades at mines
are very low (less than 0.5% Copper), large volumes of ore
need to be process to achieve an economical production
target. While obtaining these targets, the amount of energy,
water and ore transport and the management of the very
large amount of waste has become extremely expensive.
Recent research (Dunne et al., 2019 Bascur 2019 Bascur
and Gorrie 2021, Concha and Bascur, 2024) document
new strategies of operational problems to reduce the hidden
operational losses, maximize copper recovery and produc-
tion and become more proactive, using grinding, flotation
and thickening predictive models to optimize the mineral
processing plant.
It is well known that in processing low grade ore it is
very important to reduce the variability in the grinding cir-
cuit to get the right grind cut point and a particle size distri-
bution in the mill that improves the water recovery rate in
the tailing thickeners. The metal production maximization
strategy is shown in Figure 4 with its left side exhibiting each
of the process units in operation to obtain the best results
in the rougher flotation recovery. On the right side, the fig-
ure shows the well know flotation elephant curve showing
that minerals in very small and large particles sizes do not
concentrate well in traditional flotation equipment (Lynch
et al., 1981 Trahar 1981, Bascur, Herbst and Freeh, 1986,
Bascur, 1991a). Figure 4 also shows the required reduction
of product size variability to be able to move closer to maxi-
mum net production targets in flotation. It is also known
that particles with a wide size distribution do not flocculate
well and are carried out with the overflow water in thicken-
ers. These examples make particle size distribution in the
flotation and thickener’s feed a major target.
Figure 5 shows how the OSB DIGIT unit operation
template is used to model the whole plant for production
variance improvement and coordination. This data model
enables us to identify the process constraints to satisfy the
Figure 3. Typical mineral processing plant
