The use of machine vision to predict flotation performance
Centre for Minerals Research, Department of Chemical Engineering, University of Cape Town, Private Bag, Rondebosch, Cape Town 7701, South Africa; University of Queensland, Sustainable Minerals Institute, Julius Kruttschnitt Mineral Research Centre, QLD 4072, Australia
Machine vision has been proposed as an ideal non-intrusive instrument to obtain meaningful information relating to the performance of the froth phase of flotation for the purposes of process control. Many attempts have been made to use machine vision to predict performance factors such as mass recovery rate and concentrate grade. These approaches have largely been empirical, and have been shown to be accurate under limited operating conditions. The most commonly used application of machine vision technology is the measurement of froth velocity within a control strategy to balance the concentrate recovery rate down a bank by manipulating either froth depth or air rate. This paper investigates whether the measurement of physical machine vision measurements are able to provide accurate measures of mass recovery rate and concentrate grade across variations in operating conditions. The results show that although good relationships are found in narrow conditions, a mechanistic understanding and model is needed to determine relationships that are useful over a wide range of operating conditions. © 2012 Elsevier Ltd. All rights reserved.
Air rate; Concentrate grade; Concentrate recovery; Control strategies; Flotation froths; Flotation performance; Machine vision technologies; Mass recovery; Non-intrusive; On-line analysis; Operating condition; Performance factors; Vision measurement; Air; Froth flotation; Models; Process control; Recovery; Computer vision