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.
Highlights
Western Australian School of Mines: Minerals, Energy and Chemical Engineering, Curtin University, Department of Process Engineering, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa
This includes the measurement of bubble size distributions as a means to characterize froths [11,12,13], the measurement of froth colour, froth stability, and froth velocity patterns that are used on the control of flotation plants
This is a typical problem encountered by plant operators responsible for monitorvisually discriminate between the normal operating conditions (NOCs) and NEW images, as the bubble size distributions ing the flotation process and even if the plant not monitored by means a principal appear to be the same and colour cannot be used as is a basis for discrimination either.of
Summary
The multivariate statistical process monitoring framework, with a principal component model. Multivariate statistical process monitoring framework, with a principal component. The principal component model is constructed from features extracted from images model. Any of a number of different approaches can be followed to extract features from these images, and in this study, grey level co-occurrence matrices and local binary patterns are considered, as described in more detail followed by a discussion of the principal component model. Any of a number of different approaches can be followed to extract features from these images, and in this study, grey level co-occurrence matrices and local binary pat of terns are considered, as described in more detail followed by14a discussion of the principal component model
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