Pérez-Barnuevo, L., Lévesque, S. and Bazin, C. (2018) Automated Recognition of Drill Core Textures: A Geometallurgical Tool for Mineral Processing Prediction. Minerals Engineering, 118, 87-96.
https://doi.org/10.1016/j.mineng.2017.12.015

has been cited by the following article:

• TITLE: Development and Testing of a Method to Estimate the Mineral Composition of Ore from Chemical Assays with a View toward Geometallurgy: Application to an Iron Ore Concentrator

  AUTHORS: Laurence Boisvert, Claude Bazin, Josiane Caron, François Lavoie

  KEYWORDS: Block Model, Estimation, Geometallurgy, Iron Ore Concentrator, Mill Performance, Mineral Content, Modal

  JOURNAL NAME: Geomaterials, Vol.12 No.4, September 29, 2022

  ABSTRACT: For complex orebodies in which the valuable metal is carried by several minerals that respond differently to the concentration process, an ore block model should not be characterized solely with elemental assays, as this information is not sufficient to anticipate the mill performances. Data from an iron ore concentrator is used to demonstrate the idea. A method is then proposed to estimate the mineral contents of ore samples from elemental assays. The method can readily be extended to combine the estimation of the mineral contents in the feed of the mill with an estimation of the recovery of these minerals into the products of the concentrator. These mineral recoveries can subsequently be incorporated into a block model to predict the concentrator response to the processing of an ore block.