XXXI International Mineral Processing Congress 2024 Proceedings/Washington, DC/Sep 29–Oct 3 1451
number of samples beyond which the addition of further
samples made little or no improvement to the error in the
models. The RMSE declines more or less asymptotically to
the total error associated with geological sample heteroge-
neity, experimental error and the error associated with ore
characteristics not captured in the input data.
The use of geometallurgical principles and data science
for metallurgical sampling and test data analysis provides
a measurable and repeatable basis for sample-selection,
driven by the geological characteristics of the mineraliza-
tion. It provides an alternative to the plethora of rules of
thumb sampling procedures that have limited connection
to geological variability nor the variability of the metallur-
gical responses.
REFERENCES
Burmeister, E., and Aitken, L.M. 2012. “Sample size:
How many is enough?,” Australian Critical Care,
Vol 25(4):271–274, November 2012, doi: 10.1016
/j.aucc.2012.07.002.
Garrido M., Ortiz J.M., Sepulveda E., Farfan L., and
Townley B. 2019. “An overview of good practices in
the use of geometallurgy to support mining reserves
in copper sulfides deposits.” In Procemin GEOMET
2019. Chapter 2: Geometallurgical Characterization
And Modeling.
Giblett, A., and Morrell, S. 2016. “Process development
testing for comminution circuit design.” Minerals &
Metallurgical Processing, 2016, Vol. 33(4):172–177.
Gotelli N.J., and Ellison A.M. 2004. Primer of Ecological
Statistics. Sunderland: Sinauer Associates. 2004.
Green S.B. 1991. “How many subjects does it take to
do a regression analysis?” Multivariate Behavioral
Research. 1991 26:499–510.
Guresin, N., Lorenzen, L. and Muller, H. 2014. “The role
of sampling and metallurgical test work during mining
projects development.” XXVII International Mineral
Processing Congress..”.
Jenkins D.G., and Quintana-Ascencio P.F. 2020. “A
solution to minimum sample size for regressions.”
PLoS ONE 15(2): e0229345. doi: 10.1371/journal
.pone.0229345.
Kormos L., Sliwinski J., Oliveira J., and Hill G. 2013.
“Geometallurgical Characterisation And Representative
Metallurgical Sampling At Xstrata Process Support,”
Annual Canadian Mineral Processors Operators
Conference, Ottawa, Ontario, January 22–24, 2013.
Figure 6. RMSE values of DWi prediction against the number of samples. Linear regression (left panel)
and cubist model (right panel)
number of samples beyond which the addition of further
samples made little or no improvement to the error in the
models. The RMSE declines more or less asymptotically to
the total error associated with geological sample heteroge-
neity, experimental error and the error associated with ore
characteristics not captured in the input data.
The use of geometallurgical principles and data science
for metallurgical sampling and test data analysis provides
a measurable and repeatable basis for sample-selection,
driven by the geological characteristics of the mineraliza-
tion. It provides an alternative to the plethora of rules of
thumb sampling procedures that have limited connection
to geological variability nor the variability of the metallur-
gical responses.
REFERENCES
Burmeister, E., and Aitken, L.M. 2012. “Sample size:
How many is enough?,” Australian Critical Care,
Vol 25(4):271–274, November 2012, doi: 10.1016
/j.aucc.2012.07.002.
Garrido M., Ortiz J.M., Sepulveda E., Farfan L., and
Townley B. 2019. “An overview of good practices in
the use of geometallurgy to support mining reserves
in copper sulfides deposits.” In Procemin GEOMET
2019. Chapter 2: Geometallurgical Characterization
And Modeling.
Giblett, A., and Morrell, S. 2016. “Process development
testing for comminution circuit design.” Minerals &
Metallurgical Processing, 2016, Vol. 33(4):172–177.
Gotelli N.J., and Ellison A.M. 2004. Primer of Ecological
Statistics. Sunderland: Sinauer Associates. 2004.
Green S.B. 1991. “How many subjects does it take to
do a regression analysis?” Multivariate Behavioral
Research. 1991 26:499–510.
Guresin, N., Lorenzen, L. and Muller, H. 2014. “The role
of sampling and metallurgical test work during mining
projects development.” XXVII International Mineral
Processing Congress..”.
Jenkins D.G., and Quintana-Ascencio P.F. 2020. “A
solution to minimum sample size for regressions.”
PLoS ONE 15(2): e0229345. doi: 10.1371/journal
.pone.0229345.
Kormos L., Sliwinski J., Oliveira J., and Hill G. 2013.
“Geometallurgical Characterisation And Representative
Metallurgical Sampling At Xstrata Process Support,”
Annual Canadian Mineral Processors Operators
Conference, Ottawa, Ontario, January 22–24, 2013.
Figure 6. RMSE values of DWi prediction against the number of samples. Linear regression (left panel)
and cubist model (right panel)