Machine learning applications in minerals processing: A review

McCoy, J. T., & Auret, L. (2019). Machine learning applications in minerals processing: A review. Minerals Engineering, 132, 95-109. For those with library access the paper can be found here:

https://www.sciencedirect.com/science/article/abs/pii/S0892687518305430

Dr Auret has kindly supplied a presentation from IFAC MMM2018 in Shanghai

https://apc-smart.com/wp-content/uploads/2021/08/L-Auret-IFAC_MMM.pdf

This review aims to equip both researchers and practitioners with structured knowledge on the state of machine learning applications in mineral processing. The period reviewed is from 2004 to 2018 with data-based modelling, fault detection and diagnosis and machine vision identified as the main application categories. The main process applications are flotation, ore sorting, milling and smelting. This is a very good and up to date review of the state of the art in mineral processing. The authors specifically do not discuss control and optimisation applications. Reinforcement learning is also committed; this is certainly a growing field of research – and the subject of our next post.