1570 XXXI International Mineral Processing Congress 2024 Proceedings/Washington, DC/Sep 29–Oct 3
recoveries in the operational setting. However, it’s worth
noting that the SiO2 concentrate geometallurgical model
did not achieve the same level of accuracy as the other
parameters, suggesting the need for optimization.
The results underscore the practical applicability and
effectiveness of the Geometallurgy Program in optimizing
plant performance and resource utilization. Overall, the
Geometallurgy Program at the Vargem Grande Iron Ore
Complex represents a comprehensive approach to integrat-
ing geological, mineralogical, and metallurgical data to
enhance process efficiency and optimize resource manage-
ment in mineral processing operations. Moving forward,
continued refinement and application of these methodolo-
gies promise further advancements in mineral processing
technology and sustainable resource utilization.
REFERENCES
Ashley, K. and Callow, M. 2000. Ore variability: Exercises
in geometallurgy. Engineering and Mining Journal
201(2): 24–28.
Both, C., and Dimitrakopoulos, R. 2023. Utilisation
of geometallurgical predictions of processing plant
reagents and consumables for production scheduling
under uncertainty. International Journal of Mining,
Reclamation and Environment 37(1):21–42.
Both, C., and Dimitrakopoulos, R. 2022. Geometallurgical
prediction models of processing plant indicators
for stochastic mine production scheduling. IFAC-
PapersOnLine 55(21):162–167.
Breiman, L., Friedman, J., Olshen, R. and Stone, C. 1984.
Classification and Regression Trees. Biometrics 40: 874.
Bye, A. 2011. Case studies demonstrating value from geo-
metallurgy initiatives. In Proceedings of the International
Geometallurgy Conference. Brisbane: Australasian
Institute of Mining and Metallurgy.
validate industrially due to strategic decisions unrelated to
ore variability, making quantification difficult.
CONCLUSION
In conclusion, the results and discussions stemming from
the Geometallurgy Program at the Vargem Grande Iron
Ore Complex provide valuable insights into enhancing
mineral processing and overall plant efficiency. Through
extensive characterization and bench-scale tests, key find-
ings regarding the Fe and SiO2 content, mineral liberation,
and energy requirements have been elucidated.
The analysis revealed significant differences between
the ABO and HZT deposits, with varying behaviors
observed in desliming underflow and flotation processes.
A notable correlation between SiO2 content in the flota-
tion concentrate and the percentage of binary quartz was
evidenced through the mineralogical characterization of the
flotation products.
Statistical models developed for predicting mass recov-
ery, metallurgical recovery, energy requirements, and SiO2
content in the concentrate have proven to be robust tools.
These models, incorporating variables such as Fe grade, par-
ticle size fractions, and sample origin, demonstrated strong
predictive capabilities, as evidenced by high coefficients of
determination.
Furthermore, the spatial geometallurgical models offer
valuable insights into the spatial distribution of mass and
metallurgical recoveries, energy requirements, and SiO2
content in the concentrate across the mineral deposits.
These models highlight spatial heterogeneity within the ore
body, defining geometallurgical domains, providing cru-
cial information for resource management and operational
planning.
Validation of the geometallurgical model against indus-
trial results shows a moderate to high correlation, indicating
the model’s reliability in predicting mass and metallurgical
Figure 13. Comparative analysis of predicted mass and metallurgical recoveries vs. industrial outcomes for the subsequent
period (December 2022)
recoveries in the operational setting. However, it’s worth
noting that the SiO2 concentrate geometallurgical model
did not achieve the same level of accuracy as the other
parameters, suggesting the need for optimization.
The results underscore the practical applicability and
effectiveness of the Geometallurgy Program in optimizing
plant performance and resource utilization. Overall, the
Geometallurgy Program at the Vargem Grande Iron Ore
Complex represents a comprehensive approach to integrat-
ing geological, mineralogical, and metallurgical data to
enhance process efficiency and optimize resource manage-
ment in mineral processing operations. Moving forward,
continued refinement and application of these methodolo-
gies promise further advancements in mineral processing
technology and sustainable resource utilization.
REFERENCES
Ashley, K. and Callow, M. 2000. Ore variability: Exercises
in geometallurgy. Engineering and Mining Journal
201(2): 24–28.
Both, C., and Dimitrakopoulos, R. 2023. Utilisation
of geometallurgical predictions of processing plant
reagents and consumables for production scheduling
under uncertainty. International Journal of Mining,
Reclamation and Environment 37(1):21–42.
Both, C., and Dimitrakopoulos, R. 2022. Geometallurgical
prediction models of processing plant indicators
for stochastic mine production scheduling. IFAC-
PapersOnLine 55(21):162–167.
Breiman, L., Friedman, J., Olshen, R. and Stone, C. 1984.
Classification and Regression Trees. Biometrics 40: 874.
Bye, A. 2011. Case studies demonstrating value from geo-
metallurgy initiatives. In Proceedings of the International
Geometallurgy Conference. Brisbane: Australasian
Institute of Mining and Metallurgy.
validate industrially due to strategic decisions unrelated to
ore variability, making quantification difficult.
CONCLUSION
In conclusion, the results and discussions stemming from
the Geometallurgy Program at the Vargem Grande Iron
Ore Complex provide valuable insights into enhancing
mineral processing and overall plant efficiency. Through
extensive characterization and bench-scale tests, key find-
ings regarding the Fe and SiO2 content, mineral liberation,
and energy requirements have been elucidated.
The analysis revealed significant differences between
the ABO and HZT deposits, with varying behaviors
observed in desliming underflow and flotation processes.
A notable correlation between SiO2 content in the flota-
tion concentrate and the percentage of binary quartz was
evidenced through the mineralogical characterization of the
flotation products.
Statistical models developed for predicting mass recov-
ery, metallurgical recovery, energy requirements, and SiO2
content in the concentrate have proven to be robust tools.
These models, incorporating variables such as Fe grade, par-
ticle size fractions, and sample origin, demonstrated strong
predictive capabilities, as evidenced by high coefficients of
determination.
Furthermore, the spatial geometallurgical models offer
valuable insights into the spatial distribution of mass and
metallurgical recoveries, energy requirements, and SiO2
content in the concentrate across the mineral deposits.
These models highlight spatial heterogeneity within the ore
body, defining geometallurgical domains, providing cru-
cial information for resource management and operational
planning.
Validation of the geometallurgical model against indus-
trial results shows a moderate to high correlation, indicating
the model’s reliability in predicting mass and metallurgical
Figure 13. Comparative analysis of predicted mass and metallurgical recoveries vs. industrial outcomes for the subsequent
period (December 2022)