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Applying Geometallurgical Principles for Metallurgical
Sample Selection
Ian Lipton, Paul Greenhill, Luis Torres, Matthew Nimmo
AMC Consultants Pty Ltd
ABSTRACT: Metallurgical sampling and the results of metallurgical testwork form the quantitative basis for
prediction of the processing behaviour of ore. These predictions are critical inputs for the design and capital
and operating cost estimates for the ore processing plant, the economic evaluation of the project and the final
investment decision.
Ore characteristics such as hardness, competency, mineral content, grain size and texture, control ore behaviour
in a mineral processing circuit. Therefore, the principles for selection of metallurgical samples should be strongly
guided by orebody geology. Furthermore, only a tiny fraction of the orebody will be tested before it is mined, so
metallurgical testwork is always data-poor. In contrast, the geological sample database is likely to consist of tens
or hundreds of thousands of records.
A key principle of geometallurgy is to use the geological sample database to gain leverage from relatively few
high-cost metallurgical tests. Applying geometallurgical principles to design of metallurgical sampling pro-
grammes aligns sparse testwork data to abundant geological data and ensures that the data is suitable for the
application of data science methods to derive robust predictions of ore processing behaviour.
The selection of composite, variability, and blended samples with respect to purpose, representativity, and pre-
dictive modelling is discussed. Application of machine learning to metallurgical sample selection using mul-
tivariate data is demonstrated using case studies. The vexed question of “how many samples do we need?” is
addressed and a data-driven solution is proposed.
INTRODUCTION
Metallurgical sampling and the results of metallurgical
testwork form the quantitative basis for prediction of the
processing behaviour of ore. These predictions are critical
inputs for the design and capital cost estimates for the ore
processing plant, the estimates of mineral recoveries and
operating costs, and the economic evaluation of the project.
Ore characteristics such as hardness, competency, min-
eral content, grain size and texture, control the behaviour
of the ore in a mineral processing circuit. Therefore, the
principles for selection of metallurgical samples should be
strongly guided by the geology of the orebody. Furthermore,
only a tiny fraction of the orebody will be tested before it
is mined, so metallurgical testwork is always data-poor. In
contrast, the geological sample database is likely to consist
Applying Geometallurgical Principles for Metallurgical
Sample Selection
Ian Lipton, Paul Greenhill, Luis Torres, Matthew Nimmo
AMC Consultants Pty Ltd
ABSTRACT: Metallurgical sampling and the results of metallurgical testwork form the quantitative basis for
prediction of the processing behaviour of ore. These predictions are critical inputs for the design and capital
and operating cost estimates for the ore processing plant, the economic evaluation of the project and the final
investment decision.
Ore characteristics such as hardness, competency, mineral content, grain size and texture, control ore behaviour
in a mineral processing circuit. Therefore, the principles for selection of metallurgical samples should be strongly
guided by orebody geology. Furthermore, only a tiny fraction of the orebody will be tested before it is mined, so
metallurgical testwork is always data-poor. In contrast, the geological sample database is likely to consist of tens
or hundreds of thousands of records.
A key principle of geometallurgy is to use the geological sample database to gain leverage from relatively few
high-cost metallurgical tests. Applying geometallurgical principles to design of metallurgical sampling pro-
grammes aligns sparse testwork data to abundant geological data and ensures that the data is suitable for the
application of data science methods to derive robust predictions of ore processing behaviour.
The selection of composite, variability, and blended samples with respect to purpose, representativity, and pre-
dictive modelling is discussed. Application of machine learning to metallurgical sample selection using mul-
tivariate data is demonstrated using case studies. The vexed question of “how many samples do we need?” is
addressed and a data-driven solution is proposed.
INTRODUCTION
Metallurgical sampling and the results of metallurgical
testwork form the quantitative basis for prediction of the
processing behaviour of ore. These predictions are critical
inputs for the design and capital cost estimates for the ore
processing plant, the estimates of mineral recoveries and
operating costs, and the economic evaluation of the project.
Ore characteristics such as hardness, competency, min-
eral content, grain size and texture, control the behaviour
of the ore in a mineral processing circuit. Therefore, the
principles for selection of metallurgical samples should be
strongly guided by the geology of the orebody. Furthermore,
only a tiny fraction of the orebody will be tested before it
is mined, so metallurgical testwork is always data-poor. In
contrast, the geological sample database is likely to consist