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14 pages, 6360 KiB

Open AccessFeature PaperArticle

Monitoring of Flotation Systems by Use of Multivariate Froth Image Analysis

by Chris Aldrich and Xiu Liu

Minerals 2021, 11(7), 683; https://doi.org/10.3390/min11070683 - 25 Jun 2021

Cited by 13 | Viewed by 4403

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. Full article

(This article belongs to the Special Issue Froth Characterisation and Behaviour in Mineral Processing)

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17 pages, 2225 KiB

Open AccessArticle

Fuzzy Association Rule Based Froth Surface Behavior Control in Zinc Froth Flotation

by Jin Zhang, Zhaohui Tang, Mingxi Ai and Weihua Gui

Symmetry 2018, 10(6), 216; https://doi.org/10.3390/sym10060216 - 13 Jun 2018

Cited by 11 | Viewed by 4154

Abstract

Froth flotation is a vital mineral concentration process. Froth surface behavior is the knowledge about flotation working condition. However, in computer vision aided froth surface behavior control, there are still two challenges that need to be tackled seriously. Against the difficulty in the froth surface behavior representation, this paper proposes to combine the bubble size distribution (BSD) and froth velocity distribution. As far as we know, this is the first time that the froth velocity distribution is presented. Against the difficulty in the adaptive generation of the optimal froth surface behavior feature (optimal setpoint), this paper introduces the fuzzy apriori to mine the association rule between the current working condition and the optimal setpoint. Then, a fuzzy inference module is constructed to generate optimal setpoint for current working condition adaptively. Many validation experiments and comparison experiments demonstrate the superiority and robustness of the proposed methods. Full article

(This article belongs to the Special Issue Novel Machine Learning Approaches for Intelligent Big Data)

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