Geoscience Reference
In-Depth Information
amount of density samples available to model density, which
may be from 100 to 1000 per domain, depending on the size
and type of the deposit.
modeling relationships between grades, mineral species, and
metallurgical recoveries.
Challenges that may be posed by these variables at the
time of estimation will be discussed in Chaps. 8-10.
5.8
Geometallurgical Data
5.9
Summary of Minimum, Good and Best
Practices
Geometallurgy is a relatively new field in Mining that tries
to understand and model variables that are related to met-
allurgical performance. These may include variables that
directly or indirectly measures throughput (hardness, grind-
ability), recovery (liberation, mineral shape/texture, etc) and
concentrate quality. The importance of geometallurgy has
been understood for quite some time in some deposits, such
as limestone, iron, lateritic nickel, bauxite, manganese and
coal. In recent years it is increasingly becoming a key com-
ponent in resource models for all base and precious metal
deposits.
Geometallurgical variables can pose challenges. There are
at least two aspects that need to be considered: (a) the mea-
sured variables are usually indirect measures, or proxies, of
the metallurgical performance of interest; in some cases they
do not average linearly; and (b) many aspects of metallurgi-
cal performance are non-linearly dependent on the measured
variables. The issue of non-linearity is relevant because the
predictions are required at a very different volume (support)
than the original measurement is taken, as will be discussed
in Chap. 7. When a variable is non-linear, simply taking the
average or the tonnage-weighted average of the variable is
not correct. Another aspect that may be significant is that in
this type of predictions that extreme values are more relevant
than large-volume averages.
The more typical grade estimates can play a part in the
material characterization that is sought when predicting
metallurgical performance. For specific performance mea-
surements, additional variables may be considered; for ex-
ample, for hardness and grindability, Drop Weight Index
(DWi); Bond Work Index (BWi); in-situ density; and P80,
which characterizes the size of the throughput material,
may be combined in a linear or non-linear equation to pre-
dict tonnage per hours processed; alternatively, SPI (SAG
Power Index) may be relevant, depending on the modeling
approach taken. Sometimes geotechnical variables, such as
RQD (Rock Quality Designation), UCS (uniaxial compres-
sive test), PLT (point load test), etc may be used as proxies
to estimate throughput.
Other variables may include grades for payable ele-
ments, as well as deleterious elements that may result in
penalties for concentrates sold, as well as those impacting
on metallurgical recovery. May also include mineral species
present, mineral liberation, texture, grain sizes and size dis-
tribution curves (p80), etc. Commonly, direct tests of metal-
lurgical recovery are available, which in turn may lead to
At a minimum, the data must have a demonstrable level of
quality such that it adequately supports the resource mod-
eling objectives. Quality requirements will thus increase
from low to high as the level of detail of the resource model
increases, from initial deposit modeling, pre-feasibility,
and feasibility studies, mine planning and mine operations
support. Database quality, measured in terms of error rate,
should be better than 5 % for geologic codes and assay val-
ues. Specific issues to consider include:
a. Written procedures for data collection and handling
should be available. They should include procedures and
protocols for field work, geologic mapping and logging,
quality assurance and quality control, database construc-
tion, sample chain of custody, and documentation trail.
The procedures should include a QA/QC program for the
analytical work, including acceptance/rejection criteria
for batches of samples.
b. A detailed review of field practices and sample collection
procedures should be performed on a regular basis, to en-
sure that the correct procedures and protocols are being
followed.
c. Similarly, review of laboratory work should be an on-go-
ing process, including occasional visits to the laboratories
involved.
d. A QA/QC program should be implemented, and should
include at least pulp duplicates, standards, and blanks, as
discussed above. Samples should be controlled on a batch-
by-batch basis, and rejection criteria should be enforced.
e. Information about core recovery and sample weights for
RC drilling should be compiled and analyzed as drilling
progresses.
f. Sufficient density information should be available to char-
acterize the main geologic units. No less than 30 samples
per unit is a suggested minimum.
g. The compositing method should be adapted to the char-
acteristics of the (future) operation, and described and
justified in detail. Similarly, a description and analysis of
outliers is necessary.
In addition to the above, good practice requires that:
a. Every drill hole campaign should have a quantified degree
of uncertainty. This includes discussions and reports of
the quality and potential problems of sample collection
procedures, data handling, and the overall quality of the
computerized database.
