1464 XXXI International Mineral Processing Congress 2024 Proceedings/Washington, DC/Sep 29–Oct 3
observed with significantly lower collector requirements
and coarser grind size.
Flotation results to date have indicated that copper sul-
fide mineral surfaces are oxidized. Therefore, finer grind-
ing, than that suggested from the mineralogical analysis, is
required, and relatively high collector dosage requirements
(45 g/t). During sulfidization, bisulfide (HS-) acts as an
activator for copper oxide and oxidized copper sulfide spe-
cies by re-sulfurizing their surfaces improving their ability
to float.
Similar to copper, the recovery of other metals of inter-
est (Mo, Au, Ag) appear to be a function of oxidation, for
which the ratio of acid soluble Cu and total Cu is used as a
proxy (Figure 9). Additional work is currently underway to
better understand the relationship geological, geochemical
and mineralogical data have with the flotation response of
the ore in order to build a robust geometallurgical model.
CONCLUSIONS
The paper outlines the use of geochemical and mineralogi-
cal data to characterize the Copper World deposits. This
data coupled with DFA were used to classify skarn types
and sulphide/oxide associations, and guide the metallurgi-
cal test work programs. Preliminary relationships have been
developed linking geological, mineralogical and geochemi-
cal units to the metallurgical response. Additional work is
underway to develop robust geometallurgical models which
can be incorporated into block model to maximize the
value while minimizing the risk of the project.
REFERENCES
Aitchison, J. 1982. The statistical analysis of compositional
data. Journal of the Royal Statistical Society, Series B
(Methodological) 44 (2): 139–177.
Bhuiyan, M. Esmaieli, K., Ordóñez‐Calderón, J.C., 2019.
Application of data analytics techniques to establish
geometallurgical relationships to bond work index at
the Paracutu Mine, Minas Gerais, Brazil. Minerals 9:
302 doi:10.3390/min9050302.
Egozcue, J.J., Pawlowsky-Glahn, V., 2005a. Groups of
parts and their balances in compositional data analysis.
Mathematical Geology 37(7), 795–828.
Egozcue, J.J., Pawlowsky-Glahn, V., 2005b. CoDa-
Dendrogram: a new exploratory tool. In: Mateu-
Figueras, G. and Barcelo-Vidal, C. (Eds.) Proceedings
of the 2nd International Workshop on Compositional
Data Analysis, Universitat de Girona, ISBN
84-8458-222-1.
FasterCapital, 2023. The importance of copper in renew-
able energy technologies. https://fastercapital.com
/content/The-importance-of-copper-in-renewable
-energy-technologies.html.
Figure 8. Comparison of variability testing with and without CPS—copper recovery vs. acid soluble copper/total copper
observed with significantly lower collector requirements
and coarser grind size.
Flotation results to date have indicated that copper sul-
fide mineral surfaces are oxidized. Therefore, finer grind-
ing, than that suggested from the mineralogical analysis, is
required, and relatively high collector dosage requirements
(45 g/t). During sulfidization, bisulfide (HS-) acts as an
activator for copper oxide and oxidized copper sulfide spe-
cies by re-sulfurizing their surfaces improving their ability
to float.
Similar to copper, the recovery of other metals of inter-
est (Mo, Au, Ag) appear to be a function of oxidation, for
which the ratio of acid soluble Cu and total Cu is used as a
proxy (Figure 9). Additional work is currently underway to
better understand the relationship geological, geochemical
and mineralogical data have with the flotation response of
the ore in order to build a robust geometallurgical model.
CONCLUSIONS
The paper outlines the use of geochemical and mineralogi-
cal data to characterize the Copper World deposits. This
data coupled with DFA were used to classify skarn types
and sulphide/oxide associations, and guide the metallurgi-
cal test work programs. Preliminary relationships have been
developed linking geological, mineralogical and geochemi-
cal units to the metallurgical response. Additional work is
underway to develop robust geometallurgical models which
can be incorporated into block model to maximize the
value while minimizing the risk of the project.
REFERENCES
Aitchison, J. 1982. The statistical analysis of compositional
data. Journal of the Royal Statistical Society, Series B
(Methodological) 44 (2): 139–177.
Bhuiyan, M. Esmaieli, K., Ordóñez‐Calderón, J.C., 2019.
Application of data analytics techniques to establish
geometallurgical relationships to bond work index at
the Paracutu Mine, Minas Gerais, Brazil. Minerals 9:
302 doi:10.3390/min9050302.
Egozcue, J.J., Pawlowsky-Glahn, V., 2005a. Groups of
parts and their balances in compositional data analysis.
Mathematical Geology 37(7), 795–828.
Egozcue, J.J., Pawlowsky-Glahn, V., 2005b. CoDa-
Dendrogram: a new exploratory tool. In: Mateu-
Figueras, G. and Barcelo-Vidal, C. (Eds.) Proceedings
of the 2nd International Workshop on Compositional
Data Analysis, Universitat de Girona, ISBN
84-8458-222-1.
FasterCapital, 2023. The importance of copper in renew-
able energy technologies. https://fastercapital.com
/content/The-importance-of-copper-in-renewable
-energy-technologies.html.
Figure 8. Comparison of variability testing with and without CPS—copper recovery vs. acid soluble copper/total copper