7
terms of copper recovery, with C13 achieving the high-
est recovery rate of 82.4%. These collectors also exhibited
favourable copper grades and moderate mass recoveries,
indicating their potential for enhancing flotation efficiency.
Copper distribution by size in the tailings provided
additional insights into collector performance. Collectors
C9 and C8 were most effective in minimizing copper losses
in the –75 μm fraction, while C11 excelled in the +75 μm
fraction, C12 in the +150 μm fraction, and C10 in the
+210 μm fraction. These results highlight the importance
of selecting collectors based on specific size fractions to
optimize overall recovery.
Statistical analysis using PCA and HCA further vali-
dated these findings. PCA identified key performance met-
rics and relationships among collectors, with the first two
principal components capturing 91.75% of the total vari-
ance. HCA grouped the collectors into four distinct clus-
ters, with Group 1 (including C1 and C11) and Group
2 (including C8 and C13) containing the top-performing
collectors. Group 3 showed moderate performance, while
Group 4 included the least efficient collectors.
The integrated approach of combining metallurgical
and statistical analysis provided a comprehensive under-
standing of collector performance. This methodology not
only aids in the classification and selection of collectors
based on specific application needs but also suggests the
potential for synergistic combinations to improve recovery
across different particle sizes.
Future research should explore the formulation
of mixed collector systems to leverage the synergistic
effects observed and further enhance flotation efficiency.
Additionally, extending this methodology to other mineral
systems could provide broader applications and benefits in
mineral processing.
REFERENCES
[1] Bournival, G., &Ata, S. (2021). An Evaluation of the
Australian Coal Flotation Standards. Minerals, 11(6),
550.
[2] Critchley, J.K., &Riaz, M. (1991). Study of syn-
ergism between xanthate and dithiocarbamate col-
lectors in flotation of heazlewoodite. Transactions
of the Institution of Mining and Metallurgy, 100,
C55–C57.
[3] Jolliffe, I. T. (2002). Principal component analysis for
special types of data (pp. 338–372). Springer New
York.
Figure 7. Hierarchical clustering of collectors based on performance metrics
terms of copper recovery, with C13 achieving the high-
est recovery rate of 82.4%. These collectors also exhibited
favourable copper grades and moderate mass recoveries,
indicating their potential for enhancing flotation efficiency.
Copper distribution by size in the tailings provided
additional insights into collector performance. Collectors
C9 and C8 were most effective in minimizing copper losses
in the –75 μm fraction, while C11 excelled in the +75 μm
fraction, C12 in the +150 μm fraction, and C10 in the
+210 μm fraction. These results highlight the importance
of selecting collectors based on specific size fractions to
optimize overall recovery.
Statistical analysis using PCA and HCA further vali-
dated these findings. PCA identified key performance met-
rics and relationships among collectors, with the first two
principal components capturing 91.75% of the total vari-
ance. HCA grouped the collectors into four distinct clus-
ters, with Group 1 (including C1 and C11) and Group
2 (including C8 and C13) containing the top-performing
collectors. Group 3 showed moderate performance, while
Group 4 included the least efficient collectors.
The integrated approach of combining metallurgical
and statistical analysis provided a comprehensive under-
standing of collector performance. This methodology not
only aids in the classification and selection of collectors
based on specific application needs but also suggests the
potential for synergistic combinations to improve recovery
across different particle sizes.
Future research should explore the formulation
of mixed collector systems to leverage the synergistic
effects observed and further enhance flotation efficiency.
Additionally, extending this methodology to other mineral
systems could provide broader applications and benefits in
mineral processing.
REFERENCES
[1] Bournival, G., &Ata, S. (2021). An Evaluation of the
Australian Coal Flotation Standards. Minerals, 11(6),
550.
[2] Critchley, J.K., &Riaz, M. (1991). Study of syn-
ergism between xanthate and dithiocarbamate col-
lectors in flotation of heazlewoodite. Transactions
of the Institution of Mining and Metallurgy, 100,
C55–C57.
[3] Jolliffe, I. T. (2002). Principal component analysis for
special types of data (pp. 338–372). Springer New
York.
Figure 7. Hierarchical clustering of collectors based on performance metrics