XXXI International Mineral Processing Congress 2024 Proceedings/Washington, DC/Sep 29–Oct 3 933
changes in operating parameters (e.g., airflow, pulp level,
reagent dosage) with the Optimizer continuously provid-
ing, in either open-loop or closed-loop, optimised setpoints
for these parameters based on a set Value Driver function.
The Flotation Optimization App has been com-
missioned in several sites and this paper summarises the
results obtained for an iron ore mine in South America.
This innovative approach is driven by a commitment to
enhance mining process efficiency and performance, lead-
ing to improved decision-making and heightened opera-
tional value for the entirety of the circuit. Furthermore,
these developments bear substantial implications for the
enhanced recovery of iron, copper and other critical min-
erals, while simultaneously fostering sustainability through
the integration of novel mining technologies.
REFERENCES
Chen, T., and Guestrin, C. 2016. XGBoost: A Scalable
Tree Boosting System. In Proceedings of the 22nd
ACM SIGKDD International Conference on Knowledge
Discovery and Data Mining. New York:Association for
Computing Machinery. 785–794.
Gorain, B.K., Franzidis, J. P., and Manlapig, E. V. 1998.
The Empirical Prediction of Bubble Surface Area Flux
in Mechanical Flotation Cells from Cell Design and
Operating Data. Minerals Engineering 12(3):309–322.
Hart, S. 1989. Shapley Value. In Game Theory. Edited by
Eatwell, J., Milgate, M., Newman, P. The New Palgrave.
Palgrave Macmillan:210–216.
Hansen, N., Akimoto, Y., and Baudis, P. 2019. CMA-ES/
pycma on Github. Zenodo. www.zenodo.org
/records/7573532. Accessed: December 2023.
Pashkevich, D., Li, R., and Waters, K. 2023. Temperature
and climate-induced fluctuations in froth flotation: an
overview of different ore types. Canadian Metallurgical
Quarterly 62(3):511–548.
Python Software Foundation. Python Language Reference,
version 3.10. Available at http://www.python.org.
Accessed: May 2024.
Quintanilla, P., Neethling, S. J., and Brito-Parada, P. R.
2021. Modelling for froth flotation control: A review.
Minerals Engineering 162(2021):106718.
Shahbazi, R. 2011. The Empirical Prediction of Gas
Dispersion Parameters on Mechanical Flotation Cells.
Teheran: Islamic Azad University.
Yianatos, J.B., Bergh, L.G, and Aguilera, J. 2003. Flotation
scale up: use of separability curves. Minerals Engineering
16(4):347–352.
Yoon, R. H., and Luttrell, G.H. 1989. The Effect of Bubble
Size on Fine Particle Flotation. Mineral Processing and
Extractive Metallurgy Review 5(1):101–122.
Wills, B. A., and Napier-Munn, T. 2005. Wills’ Mineral
Processing Technology. Butterworth-Heinemann.
p. 267–352.
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XXXI International Mineral Processing Congress 2024 Proceedings/Washington, DC/Sep 29–Oct 3 933
changes in operating parameters (e.g., airflow, pulp level,
reagent dosage) with the Optimizer continuously provid-
ing, in either open-loop or closed-loop, optimised setpoints
for these parameters based on a set Value Driver function.
The Flotation Optimization App has been com-
missioned in several sites and this paper summarises the
results obtained for an iron ore mine in South America.
This innovative approach is driven by a commitment to
enhance mining process efficiency and performance, lead-
ing to improved decision-making and heightened opera-
tional value for the entirety of the circuit. Furthermore,
these developments bear substantial implications for the
enhanced recovery of iron, copper and other critical min-
erals, while simultaneously fostering sustainability through
the integration of novel mining technologies.
REFERENCES
Chen, T., and Guestrin, C. 2016. XGBoost: A Scalable
Tree Boosting System. In Proceedings of the 22nd
ACM SIGKDD International Conference on Knowledge
Discovery and Data Mining. New York:Association for
Computing Machinery. 785–794.
Gorain, B.K., Franzidis, J. P., and Manlapig, E. V. 1998.
The Empirical Prediction of Bubble Surface Area Flux
in Mechanical Flotation Cells from Cell Design and
Operating Data. Minerals Engineering 12(3):309–322.
Hart, S. 1989. Shapley Value. In Game Theory. Edited by
Eatwell, J., Milgate, M., Newman, P. The New Palgrave.
Palgrave Macmillan:210–216.
Hansen, N., Akimoto, Y., and Baudis, P. 2019. CMA-ES/
pycma on Github. Zenodo. www.zenodo.org
/records/7573532. Accessed: December 2023.
Pashkevich, D., Li, R., and Waters, K. 2023. Temperature
and climate-induced fluctuations in froth flotation: an
overview of different ore types. Canadian Metallurgical
Quarterly 62(3):511–548.
Python Software Foundation. Python Language Reference,
version 3.10. Available at http://www.python.org.
Accessed: May 2024.
Quintanilla, P., Neethling, S. J., and Brito-Parada, P. R.
2021. Modelling for froth flotation control: A review.
Minerals Engineering 162(2021):106718.
Shahbazi, R. 2011. The Empirical Prediction of Gas
Dispersion Parameters on Mechanical Flotation Cells.
Teheran: Islamic Azad University.
Yianatos, J.B., Bergh, L.G, and Aguilera, J. 2003. Flotation
scale up: use of separability curves. Minerals Engineering
16(4):347–352.
Yoon, R. H., and Luttrell, G.H. 1989. The Effect of Bubble
Size on Fine Particle Flotation. Mineral Processing and
Extractive Metallurgy Review 5(1):101–122.
Wills, B. A., and Napier-Munn, T. 2005. Wills’ Mineral
Processing Technology. Butterworth-Heinemann.
p. 267–352.

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