Geoscience Reference
In-Depth Information
Table 13.2
TCu performance factors of the ID
3
method by destination relative to the SGS reference model
Destination
Tonnage
(Dest./Reference)
TCu Grade
(Dest./Reference)
Cu Metal Content Cu
(Dest./Reference)
SAL
1.10
0.91
0.99
SME
1.16
1.06
1.22
SBA
0.18
1.15
0.21
SMR
0.50
1.36
0.68
SAS
0.55
1.02
0.56
OXA
1.29
0.85
1.10
OXB
1.16
1.08
1.25
OXL
0.44
1.54
0.68
MIX
0.52
0.90
0.47
TOTAL
1.16
0.84
0.98
T
able 13.3
TCu performance factors of the BEI method by destination relative to the SGS reference model
Destination
Tonnage
(Dest./Reference)
TCu Grade
(Dest./Reference)
Cu Metal Content Cu
(Dest./Reference)
SAL
1.10
0.92
1.00
SME
1.09
1.00
1.09
SBA
0.45
1.01
0.45
SMR
0.43
1.01
0.44
SAS
0.87
0.95
0.82
OXA
1.13
0.93
1.05
OXB
1.98
0.98
1.94
OXL
1.49
1.41
2.10
MIX
0.71
0.78
0.55
TOTAL
1.11
0.89
0.99
Only the results for the ID
3
and BEI methods are present-
ed here. The simulation-based method produced similar and
slightly better results compared to the BEI method, but it is
more complicated and slower to implement. The OK method
produced marginally worse results.
Tables
13.2
and
13.3
show the relative performance of
the ID
3
and BEI methods with respect to the reference model
for tonnages, TCu grade, Cu metal content, and revenues.
The closer the value to 1.0, the better the method reproduces
the to reference model, and, by extension and within the ap-
proximations of the reference model calibrations, actual pro-
duction. A factor greater than 1 implies overestimation with
respect the reference model. The destinations corresponding
to waste, SSM, and OXM are not shown due to the low ton-
nages produced within the evaluation period. The overall
ore and marginal ore production for the period was about
59.5 million tons, so the statistical mass available for com-
parison is significant.
Note how for most destinations and variables considered,
the BEI method is superior. Recall that a 1 % difference
between the two methods represents close to 600,000 met-
ric tons of ore, or about 10,000 metric tons of contained Cu.
Considering the depressed Cu prices at the time, a 1 % differ-
ence in contained Cu represented about US$ 16 million. At
2013 copper prices, the dollar value of the difference would
be between US$ 70 and 80 million. In most cases, even
though the differences in percentage points may be small,
they represent significant economic improvements given the
size of the operation.
The added economic benefit of the BEI method
results from virtually no additional expenditure, since all
operational practices remain the same. Also, the panel
drawing process is facilitated by the use of smaller blocks
and less sharp corners (Figs.
13.6
and
13.7
). This in turn
results in less unplanned operational dilution, because the
shovels will extract the material following more faithfully
the delineated zones. Although real, this effect is more
difficult to quantify.
13.4
Selection of Ore and Waste:
Simulation-based Methods
The objective of the simulation-based methodologies is to
optimally select ore from waste according to different op-
timality criteria. Also, it provides more flexibility to handle
several destinations for recoverable material, including ore
blending with different metallurgical responses. Minimum-
variance algorithms such as kriging have traditionally been
the optimization criteria in most geostatistical applications,
but are not always appropriate (Srivastava
1987
).
In open pit and underground grade control, optimization
should always be based on maximizing the economic
value of the recovered material. The material selected for
