Performance Improvements during Mineral Processing Using Material Fingerprints Derived from Machine Learning—A Conceptual Framework

Performance Improvements during Mineral Processing Using Material Fingerprints Derived from Machine Learning—A Conceptual Framework

Material attributes (e.g., chemical composition, mineralogy, texture) are identified as the causative source of variations in the behaviour of mineral processing. That makes them suitable to act as key characteristics to characterise and classify material. Therefore, vast quantities of collected dat...

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Bibliographic Details
Main Authors: Jeroen R. van Duijvenbode, Mike W.N. Buxton, Masoud Soleymani Shishvan
Format: Article
Language:English
Published: MDPI AG 2020-04-01
Series:Minerals
Subjects:
data confidence
machine learning
material fingerprints
mineral processing
behavioural prediction, mining
Online Access:https://www.mdpi.com/2075-163X/10/4/366

Internet

https://www.mdpi.com/2075-163X/10/4/366

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