2566 XXXI International Mineral Processing Congress 2024 Proceedings/Washington, DC/Sep 29–Oct 3
CONCLUSION
The methodology presented in this contribution provides
a systematic approach to upscale and optimize reagent
systems in froth flotation processes, minimizing the num-
ber of tests required. While it offers a well-defined work-
flow, users still have flexibility to adapt the experimental
work to their specific needs, constraints, and objectives.
However, it is important to note that this methodology
does not replace the theoretical and technical knowledge
needed for the flotation process being studied. Instead, it
serves as a robust approach for upscaling and optimization
tasks saving time and resources. One of its key features is
its flexibility: Parameters that have already been optimized
in batch flotation can be selected based not only on their
effects on the target variables being investigated, but also on
process knowledge and limitations in time and resources.
The workflow was demonstrated in a low grade scheelite ore
case study. The case study not only explored the feasibility
of using colloidal silica as a calcite depressant in scheelite
flotation, but also systematically optimized and upscaled.
Nevertheless, the significance of these positive results found
on the industrial scale should be investigated in industrial
long-term tests over weeks or months.
REFERENCES
Ahn, H. (2015). Central Composite Design for the
Experiments with Replicate Runs at Factorial and Axial
Points. Industrial Engineering, Management Science
and Applications 2015: 969–979.
Ben Said, B., L. Pereira, T. Saeed, et al. (Asia-Pacific
2022). Development of a user-friendly R Platform for
Optimizing Mineral Processing. International Mineral
Processing Congress. Melbourne, Australia.
Fandrich, R., Y. Gu, D. Burrows, et al. (2007). “Modern
SEM-based mineral liberation analysis.” International
Journal of Mineral Processing 84(1–4): 310–320.
Kupka, N., B. Babel and M. Rudolph (2020). “The poten-
tial role of colloidal silica as a depressant in scheelite
flotation.” Minerals 10(2): 144.
Nagaraj, D.R. (2005). “Reagent selection and optimi-
zation—the case for a holistic approach.” Minerals
Engineering 18(2): 151–158.
Nagaraj, D.R. and R.S. Farinato (2016). “Evolution
of flotation chemistry and chemicals: A century of
innovations and the lingering challenges.” Minerals
Engineering 96–97: 2–14.
Nelder, J.A. and R. Mead (1965). “A Simplex Method for
Function Minimization “ The Computer Journal 7(4):
308–313.
0 20 40 60 80
28
30
32
34
36
38
40
42
WO
3
grade (%)
WO3 recovery (%)
Time (min)
84,0
84,5
85,0
85,5
86,0
86,5
87,0
87,5
88,0
88,5
Figure 16. Time curves of WO
3 grade and recovery during
the industrial trial with optimal parameter settings
CONCLUSION
The methodology presented in this contribution provides
a systematic approach to upscale and optimize reagent
systems in froth flotation processes, minimizing the num-
ber of tests required. While it offers a well-defined work-
flow, users still have flexibility to adapt the experimental
work to their specific needs, constraints, and objectives.
However, it is important to note that this methodology
does not replace the theoretical and technical knowledge
needed for the flotation process being studied. Instead, it
serves as a robust approach for upscaling and optimization
tasks saving time and resources. One of its key features is
its flexibility: Parameters that have already been optimized
in batch flotation can be selected based not only on their
effects on the target variables being investigated, but also on
process knowledge and limitations in time and resources.
The workflow was demonstrated in a low grade scheelite ore
case study. The case study not only explored the feasibility
of using colloidal silica as a calcite depressant in scheelite
flotation, but also systematically optimized and upscaled.
Nevertheless, the significance of these positive results found
on the industrial scale should be investigated in industrial
long-term tests over weeks or months.
REFERENCES
Ahn, H. (2015). Central Composite Design for the
Experiments with Replicate Runs at Factorial and Axial
Points. Industrial Engineering, Management Science
and Applications 2015: 969–979.
Ben Said, B., L. Pereira, T. Saeed, et al. (Asia-Pacific
2022). Development of a user-friendly R Platform for
Optimizing Mineral Processing. International Mineral
Processing Congress. Melbourne, Australia.
Fandrich, R., Y. Gu, D. Burrows, et al. (2007). “Modern
SEM-based mineral liberation analysis.” International
Journal of Mineral Processing 84(1–4): 310–320.
Kupka, N., B. Babel and M. Rudolph (2020). “The poten-
tial role of colloidal silica as a depressant in scheelite
flotation.” Minerals 10(2): 144.
Nagaraj, D.R. (2005). “Reagent selection and optimi-
zation—the case for a holistic approach.” Minerals
Engineering 18(2): 151–158.
Nagaraj, D.R. and R.S. Farinato (2016). “Evolution
of flotation chemistry and chemicals: A century of
innovations and the lingering challenges.” Minerals
Engineering 96–97: 2–14.
Nelder, J.A. and R. Mead (1965). “A Simplex Method for
Function Minimization “ The Computer Journal 7(4):
308–313.
0 20 40 60 80
28
30
32
34
36
38
40
42
WO
3
grade (%)
WO3 recovery (%)
Time (min)
84,0
84,5
85,0
85,5
86,0
86,5
87,0
87,5
88,0
88,5
Figure 16. Time curves of WO
3 grade and recovery during
the industrial trial with optimal parameter settings