Acoustic Sensing and Supervised Machine Learning for In Situ Classification of Semi-Autogenous (SAG) Mill Feed Size Fractions Using Different Feature Extraction Techniques

Acoustic Sensing and Supervised Machine Learning for In Situ Classification of Semi-Autogenous (SAG) Mill Feed Size Fractions Using Different Feature Extraction Techniques

The harsh and hostile internal environment of semi-autogenous (SAG) mills renders real-time monitoring of some critical variables practically unmeasured. Typically, feed size fractions are known to cause mill fluctuations and impede the consistent processing behaviour of ores. There is, therefore, t...

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Bibliographic Details
Main Authors: Kwaku Boateng Owusu, William Skinner, Richmond K. Asamoah
Format: Article
Language:English
Published: MDPI AG 2023-04-01
Series:Powders
Subjects:
semi-autogenous mill
acoustic sensing
supervised machine learning classification algorithms
feature extraction
mill feed size fraction
Online Access:https://www.mdpi.com/2674-0516/2/2/18

Internet

https://www.mdpi.com/2674-0516/2/2/18

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