Published: 20 August 2024
XXXI IMPC-International mineral processing congress 2024
AMC Consultants is excited to present at the upcoming International Mineral Processing Congress (IMPC) Congress 2024, taking place in Washington, DC, United States from Sunday, September 29 to Thursday, October 3, 2024.
Dr
Paul Greenhill, Principal Consultant, will present on Applying Geometallurgical Principles for Metallurgical Sample Selection during the
Geometallurgy and Process Mineralogy I – Using Geological Data to Inform Metallurgical Processes session.
IMPC 2024 will focus on "Mineral Processing for the Energy Transition" and will bring together global industry experts to explore key research and advances in science and technology. This congress is dedicated to the critical role that mineral processing plays in securing a sustainable and cost-effective supply of a wide range of minerals for the future.
Applying Geometallurgical Principles for Metallurgical Sample Selection
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 programmes 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 predictive modelling is discussed. Application of machine learning to metallurgical sample selection using multivariate 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.
Authors:
- Paul Greenhill, Principal Consultant
- Ian Lipton, Principal Geologist
- Luis Torres, Senior Data Scientist
- Matthew Nimmo, Principal Geoscience and Data Scientist
Date:
Thursday, October 3, 2024
Time: 2.30pm
AMC's Speaker at IMPC Congress 2024
Dr Paul Greenhill
Principal Consultant
About Paul
With 30 years’ experience in senior management, project management, techno-financial modelling, project studies and operational consulting, research management and technology evaluation. Paul’s primary expertise is mineral processing and pyrometallurgy applied to feasibility study evaluation, techno-economic modeling/evaluation, technical due diligence and business improvement.
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