D.W. Moolman

The interpretation of flotation froth surfaces by using digital image analysis and neural networks

Abstract The rapid developments in computer vision, computational resources and artificial intelligence, and the integration of these technologies are creating new possibilities in the design and implementation of commercial machine vision systems. In chemical and minerals engineering, numerous opportunities for the application of these systems exist, of which the characterization of flotation froth structures is a good example of the utilization of visual data as a supplement to conventional plant data. In this paper images from pyrite batch flotation tests conducted after a factorial design as well as images from a copper flotation plant were used to understand the relationship between froth characteristics and flotation performance better. The results show that a significant amount of data can be extracted from flotation surface froths. Techniques have been developed to characterize chromatic information, average bubble size, froth texture, froth stability and mobility of surface froths. It has been shown that most of the froth characteristics of this study can be explained in terms of the concentration of solids in the froth and the factors that affect the solids concentration. The techniques developed proved to be useful in investigating the effect of a mixed collector and the addition of copper sulphate. The depressing effect of the copper sulphate and the higher grades and recoveries made possible by the mixed collector under these conditions were explained by analysis of the froth features. Excellent results were obtained in modelling the relation between froth characteristics or froth grade and recovery by using a backpropagation neural network. A sensitivity analysis showed that the most important froth features for the experimental conditions of this study are the froth stability, mobility and average bubble size. This computer vision system constitutes a powerful research tool for the investigation and interpretation of the effect of various flotation parameters. This paper also shows how the rapid development in computer technology and related disciplines can be used to transform recently developed concepts and available technology into a new generation of intelligent automation systems.

Digital image processing as a tool for on-line monitoring of froth in flotation plants

Abstract As the most important separation technique in mineral processing, flotation has been the subject of intensive investigation over many years, but despite these efforts it remains a poorly understood process that defies generally useful mathematical modelling. As a result the control of industrial flotation plants is often based on the visual appearance of the froth phase, and depends to a large extent on the experience and ability of a human operator. These types of processes are consequently often controlled suboptimally owing to high personnel turnover, lack of fundamental understanding of plant dynamics, inaccuracy or unreliability of manual control systems, etc. By using techniques based on image colour analysis and Fast Fourier Transforms to process videographic data of the froth phase in a copper flotation plant, it is shown that an image processing system can distinguish between different copper levels in the froth down a rougher bank and extract global features from the visual characteristics of the surface froth. In this way it is possible to quantify the mineral content of the froth (based on colour), the average bubble size distribution, the direction of flow and the shape of the bubbles or the mobility of the froth. The overall image of the froth is analysed instead of attempting to identify the boundaries between bubbles.

The significance of flotation froth appearance for machine vision control

Abstract The development of robust automatic control systems has proved difficult because of the complexity of the problem. Flotation is notorious for its susceptibility to process upsets and consequently its poor performance, making successful flotation control systems an elusive goal. Machine vision systems provide a novel solution to several of the problems encountered in conventional flotation systems for monitoring and control. In previous work powerful techniques have been developed for the extraction of flotation froth appearance features such as average bubble size, froth mobility and stability, chromatic information and textural properties of surface froth. A methodology has been developed for the classification of froths, based on appearance and metallurgical significance. The objective of this paper is to provide a clear framework and motivation for the development of a machine vision system for flotation control. A systematic discussion of the diffuse literature descriptions about the relation between froth appearance and fundamental flotation principles is presented. A preliminary classification strategy for flotation froths is proposed and an example of how process deviations can be related to froth appearance is provided. Design constraints and principles imposed on a vision system by flotation are also discussed.

The classification of froth structures in a copper flotation plant by means of a neural net

Abstract By making use of grey level dependence matrix methods, digitized images of the froth phases in a copper flotation plant were reduced to feature vectors without losing essential information of the characteristics of the froth. Classification of features extracted by means of both spatial grey level dependence matrix (SGLDM) methods, as well as neighbouring grey level dependence matrix (NGLDM) methods was investigated. By using a learning vector quantization (LVQ) neural net it was shown that froth structures could be classified satisfactorily when either NGLDM or SGLDM methods were used. When these feature sets were combined, however, the success rate of classification improved to almost 90%. This is sufficiently accurate to enable incorporation of the neural net classifier into on-line plant control systems.

The interrelationship between surface froth characteristics and industrial flotation performance

Abstract This paper discusses the rapid development in computer technology and neural networks that are used to transform recently developed concepts and available technology into a new generation of intelligent automation systems. In this study features extracted from images of froths by an on-line machine vision system in an industrial precious metal flotation plant were used to relate froth characteristics with the performance of the plant by using self-organising and Sammon maps. This intelligent vision system constitutes a powerful tool for the investigation and interpretation of the effect of various flotation parameters. Previous work is extended by relating surface froth characteristics with industrial flotation control and performance variables. This method of system identification represents a significant development towards an automatic control system.

The monitoring of froth surfaces on industrial flotation plants using connectionist image processing techniques

Abstract The rapid development of computer vision, computational resources, artificial intelligence and the integration of these technologies are creating new possibilities in the design and implementation of commercial machine vision systems. In minerals engineering numerous opportunities for the application of these systems exist, such as the characterization of flotation froth structures which is discussed in this paper by way of example. A general model for the development of feasible, real-time machine vision systems is proposed, which is based on an analogy with biological visual perception made possible by a connectionist approach and the ability of neural networks to solve ill-posed problems. It is shown that both supervised and unsupervised neural nets can be used in different ways to analyze froth structures of industrial flotation cells. Unsupervised (self-organizing) neural nets can monitor process behaviour on a continuous rather than on a discrete basis, which makes the early detection of erratic process control possible. Since some losses in information are incurred with the use of self-organizing systems, intelligent monitoring and control systems would in practice probably be comprised of both types of neural nets.

Relationship between surface froth features and process conditions in the batch flotation of a sulphide ore

Flotation processes occurring in the bulk and froth phases have a characteristic influence on the structural features and dynamics of the flotation froth. In principle the froth features can therefore be used as a useful indicator of the performance of the flotation system. In this study the surface froth features and dynamics are represented by three features extracted from the digitized images of the froths, viz. a statistical feature which is a rough indication of the average bubble size of the froth, a measure of the froth stability, as well as the average grey level of the froth, which is an indication of mineral loading. The effect of high intensity conditioning on the batch flotation of a sulphide ore from the Merensky reef in South Africa was investigated, and the significantly beneficial effect of high intensity conditioning on the performance of the flotation was clearly reflected in the smaller bubble size distributions and greater stability of the flotation froths.

CHARACTERIZATION OF FLOTATION PROCESSES WITH SELF-ORGANIZING NEURAL NETS

Flotation processes are difficult to describe fundamentally, owing to the stochastic nature of the froth structures and the ill-defined chemorheology of the froth. Considerable information on the process is reflected by the structure of the froth. In previous work it has been shown that structural features extracted from flotation froths can be related to the behavior of flotation processes in a qualitative way through the identification of certain behavioral regimes or classes by using a supervised neural net as classifier. Although useful as an aid to control decisions, this method is less suitable for quantitative or dynamic analysis of the behavior of flotation plants. In this paper a new method for the analysis of flotation plants is consequently proposed, based on the use of order preserving maps of features extracted from digitized images of the froth phase. The construction of these maps by means of a self-organizing neural net is demonstrated by way of examples concerning the analysis of industri...

Machine learning strategies for control of flotation plants

Abstract Although flotation processes are notoriously difficult to model from first principles, knowledge-based systems can be used to great advantage to monitor and control plants, provided that process knowledge can be captured effectively on the plant. By making use of machine learning techniques the features of the surface froths of flotation cells can be used to construct representations of the behaviour of a plant. Two probabilistic decision tree methods and a backpropagation neural net were all equally capable of classifying the different froths at least as well as a human expert. Explicit decision trees were derived, relating froth characteristics to froth surface structures. Relatively sharply clustered Sammon maps of froth structures were obtained, allowing good visualisation of multidimensional flotation data.

Visualisation of plant disturbances using self-organising maps

Ill-defined processes such as the froth flotation of minerals are mostly controlled in an empirical way by using rules of thumb. These processes involve so many variables that the plant operator finds it difficult to visualise or even observe a change in process conditions. In froth flotation the operator is supposed to visually observe process changes from the appearance of the froth, which is an unreasonable demand under industrial conditions. An on-line computer vision system based on a textural analysis of the froth phase has been developed in South Africa and has been in operation on two industrial plants since early 1995. Textural parameters are determined on-line, and disturbances in process conditions, such as a change in reagent addition or froth depth, are visualised via a Self-Organizing Map (SOM) neural net.