Using machine learning tools and multivariate analysis for metallurgical sample selection and Predictive Geometallurgy

Speaker: Dr Paul Greenhill

Abstract:  A key principle of geometallurgy is to use the geological sample database to gain leverage from relatively few, high cost metallurgical tests. Using geometallurgical principles to design 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.

Bio: Paul Greenhill is a Principal Consultant in the Geometallurgy group of AMC Consultants and provides metallurgical expertise across AMC’s business. His career has included post-doctoral research at the University of Edinburgh, Senior Research Scientist at Comalco Limited on advanced aluminium smelting technologies and technical support. 13 years with AMIRA International including Chief Operating Officer and Research Director. Subsequently, Paul took over Managing Director of RMDStem Limited, a mining and technology consultancy. He worked independently as a consultant in minerals and technology before joining AMC in 2018. Paul is a PhD trained physical and theoretical chemist.