3648 XXXI International Mineral Processing Congress 2024 Proceedings/Washington, DC/Sep 29–Oct 3
ratio and drop back for a given flotation stage. In
simple terms, flotation efficiency reflects the ability
to upgrade with as little drop back of valuables as
possible. If the overall concentration ratio of a flota-
tion circuit is 50 with drop back of 10%, then FE is
5. Flotation efficiency provides critical information
of the process and is applicable to both cells-down-
the-bank or for any cleaning stage.
• Flotation selectivity (FS): Flotation selectivity is the
ratio of flotation efficiency of one mineral compared
to another. If the FE of copper and iron minerals
is 5 and 2, respectively, then FS is 2.5. The higher
the value of FS, the better the selectivity of the flota-
tion process. Flotation selectivity complements the
flotation efficiency information when applied over a
number of mineral species in an ore.
Figures 2a and 2b show that it is possible to model both the
rougher and the cleaner stages at the same time for both
bench and plant operating data, which is an important
aspect of this integrated approach.
In a green-field situation, certain non-kinetics based
relationships are developed based on bench tests such as
rougher kinetics, open cleaners and locked cycles, that allow
prediction of concentrate mass recoveries needed to target
overall concentration or enrichment ratio in the rougher
and cleaner stages and their respective metal recoveries.
Mass recoveries are then converted into carrying rates (solids
and slurry) for each rougher and cleaner stage as required.
Benchmarking through a large cell operating database then
allows selection of cell types, sizes and cell numbers for
the rougher and cleaner stages. In a brown-field situation,
however, plant operating data is utilized to select cell car-
rying rate parameters for estimation of flotation capacity.
Once the flotation capacity is determined from the
non-kinetics based macro flotation modelling, then a direct
comparison of flotation capacity estimated from both the
traditional and modelling &simulation based approaches
is carried out as shown in Table 2.
In this case, the three methods predicted reasonably
close to each other, though this may not always be the case.
In situations where the deviations are significant, a detailed
understanding of the assumptions along with any insight
on the challenges associated with ore type is needed to
make a decision on the final design. It is best that the three
approaches are carried out independently to avoid strong
biases. Sensitivity analysis is carried out if the confidence
level of certain assumptions made is particularly low. The
final decision on flotation capacity selection is normally
based on risk-benefit analysis, ore complexity, uniqueness
in application, cell type and cost considerations.
CONCLUSIONS
An integrated approach to flotation scale-up and plant
design has been developed and validated for both greenfield
and brownfield projects. This approach integrates three dif-
ferent techniques viz. the traditional, the flotation kinetics
modelling &simulation route and the non-kinetics-based
method. This allows cross-validation between the three
techniques, and any major deviations in the design out-
come from these three techniques lead to better introspec-
tion and insight finally resulting in a high-confidence plant
design, especially for complex ore bodies where there is no
precedence or limited benchmarking. This is an important
Table 2. Estimation of flotation capacity using an integrated approach
Circuits Model Types Solids%
Capacity Estimation Actual Design
Total# of Cells Cell Size, m3 Total #of Cells
Rougher Traditional 35 300 6
7 Model &Sim 35 300 8
Non-Kinetics 35 300 7
1st Cleaner Traditional 17.5 130 5
5 Model &Sim 17.5 130 5
Non-Kinetics 17.5 130 4
1st Cleaner Scavenger Traditional 21 130 5
4 Model &Sim 21 130 4
Non-Kinetics 21 130 4
2nd Cleaner Traditional 12.5 50 8
7 Model &Sim 12.5 50 7
Non-Kinetics 12.5 50 7
3rd Cleaner Traditional 10 50 4
5 Model &Sim 10 50 6
Non-Kinetics 10 50 5
ratio and drop back for a given flotation stage. In
simple terms, flotation efficiency reflects the ability
to upgrade with as little drop back of valuables as
possible. If the overall concentration ratio of a flota-
tion circuit is 50 with drop back of 10%, then FE is
5. Flotation efficiency provides critical information
of the process and is applicable to both cells-down-
the-bank or for any cleaning stage.
• Flotation selectivity (FS): Flotation selectivity is the
ratio of flotation efficiency of one mineral compared
to another. If the FE of copper and iron minerals
is 5 and 2, respectively, then FS is 2.5. The higher
the value of FS, the better the selectivity of the flota-
tion process. Flotation selectivity complements the
flotation efficiency information when applied over a
number of mineral species in an ore.
Figures 2a and 2b show that it is possible to model both the
rougher and the cleaner stages at the same time for both
bench and plant operating data, which is an important
aspect of this integrated approach.
In a green-field situation, certain non-kinetics based
relationships are developed based on bench tests such as
rougher kinetics, open cleaners and locked cycles, that allow
prediction of concentrate mass recoveries needed to target
overall concentration or enrichment ratio in the rougher
and cleaner stages and their respective metal recoveries.
Mass recoveries are then converted into carrying rates (solids
and slurry) for each rougher and cleaner stage as required.
Benchmarking through a large cell operating database then
allows selection of cell types, sizes and cell numbers for
the rougher and cleaner stages. In a brown-field situation,
however, plant operating data is utilized to select cell car-
rying rate parameters for estimation of flotation capacity.
Once the flotation capacity is determined from the
non-kinetics based macro flotation modelling, then a direct
comparison of flotation capacity estimated from both the
traditional and modelling &simulation based approaches
is carried out as shown in Table 2.
In this case, the three methods predicted reasonably
close to each other, though this may not always be the case.
In situations where the deviations are significant, a detailed
understanding of the assumptions along with any insight
on the challenges associated with ore type is needed to
make a decision on the final design. It is best that the three
approaches are carried out independently to avoid strong
biases. Sensitivity analysis is carried out if the confidence
level of certain assumptions made is particularly low. The
final decision on flotation capacity selection is normally
based on risk-benefit analysis, ore complexity, uniqueness
in application, cell type and cost considerations.
CONCLUSIONS
An integrated approach to flotation scale-up and plant
design has been developed and validated for both greenfield
and brownfield projects. This approach integrates three dif-
ferent techniques viz. the traditional, the flotation kinetics
modelling &simulation route and the non-kinetics-based
method. This allows cross-validation between the three
techniques, and any major deviations in the design out-
come from these three techniques lead to better introspec-
tion and insight finally resulting in a high-confidence plant
design, especially for complex ore bodies where there is no
precedence or limited benchmarking. This is an important
Table 2. Estimation of flotation capacity using an integrated approach
Circuits Model Types Solids%
Capacity Estimation Actual Design
Total# of Cells Cell Size, m3 Total #of Cells
Rougher Traditional 35 300 6
7 Model &Sim 35 300 8
Non-Kinetics 35 300 7
1st Cleaner Traditional 17.5 130 5
5 Model &Sim 17.5 130 5
Non-Kinetics 17.5 130 4
1st Cleaner Scavenger Traditional 21 130 5
4 Model &Sim 21 130 4
Non-Kinetics 21 130 4
2nd Cleaner Traditional 12.5 50 8
7 Model &Sim 12.5 50 7
Non-Kinetics 12.5 50 7
3rd Cleaner Traditional 10 50 4
5 Model &Sim 10 50 6
Non-Kinetics 10 50 5