Monitoring of Flotation Systems by Use of Multivariate Froth Image Analysis

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ID: 273310
2021
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Abstract
Froth image analysis has been considered widely in the identification of operational regimes in flotation circuits, the characterisation of froths in terms of bubble size distributions, froth stability and local froth velocity patterns, or as a basis for the development of inferential online sensors for chemical species in the froth. Relatively few studies have considered flotation froth image analysis in unsupervised process monitoring applications. In this study, it is shown that froth image analysis can be combined with traditional multivariate statistical process monitoring methods for reliable monitoring of industrial platinum metal group flotation plants. This can be accomplished with well-established methods of multivariate image analysis, such as the Haralick feature set derived from grey level co-occurrence matrices and local binary patterns that were considered in this investigation.
Reference Key
aldrich2021mineralsmonitoring Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Chris Aldrich;Xiu Liu;Aldrich, Chris;Liu, Xiu;
Journal minerals
Year 2021
DOI
10.3390/min11070683
URL
https://www.mdpi.com/2075-163X/11/7/683/htm
Keywords
principal component analysis flotation computer vision mineral processing froth image analysis local binary patterns grey level co-occurrence matrices multivariate statistical process monitoring

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