Sensing and optimisation of Autogenous (AG) and Semi-autogenous (SAG) grinding mills /
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(Published version)
Date
2022
Authors
Owusu, Kwaku Boateng
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Journal Title
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Type:
thesis
Citation
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Abstract
The hostile internal environment of industrial autogenous/semi-autogenous (AG/SAG) mills makes online monitoring unreliable using direct measuring methods. To develop an efficient online monitoring system in AG/SAG mills, this thesis investigated the acoustic sensing of feed ore heterogeneity in a laboratory AG/SAG mill. The study was extended to a large-scale SAG mill, where acoustic frequencies and the Gaussian process regression (GPR) model were used to predict mill weight. It was shown that the acoustic response of the laboratory AG/SAG mill was sensitive for monitoring different feed hardness and size distributions. Also, it was established that the GPR model makes good mill weight prediction combined with relevant acoustic frequencies.
School/Discipline
University of South Australia. UniSA STEM.
UniSA Future Industries Institute
UniSA Future Industries Institute
Dissertation Note
Thesis (PhD(Minerals and Resources))--University of South Australia, 2022.
Provenance
Copyright 2022 Kwaku Boateng Owusu
Description
1 ethesis (xxviii, 311, [9] pages) :
colour illustrations and charts.
Includes bibliographical references (pages 281-297)
colour illustrations and charts.
Includes bibliographical references (pages 281-297)
Access Status
506 0#$fstar $2Unrestricted online access