J.S.J. Van Deventer

Factors affecting the mechanism of the adsorption of arsenic species on activated carbon

Abstract The development of carbon-in-pulp (CIP) and resin-in-pulp (RIP) processes sparked off intensive investigations into the kinetics and mechanism of adsorption of various species onto activated carbon. The increased processing of complex sulphide ores (e.g. the bacterial oxidation of gold-bearing arsenopyrite) has led to higher levels of impurities such as arsenic in process streams. The removal of such impurities could become a new field of application for activated carbon. It was found that As(V) is more effectively removed from solution by using activated carbon with a high ash content. Pretreatment of the carbon with Cu(II) solutions improves its arsenic removal capacity. The optimum pH for arsenic adsorption by pretreated carbon is approximately 6. There are two mechanisms of arsenic adsorption which occur simultaneously. The arsenic in solution can form insoluble metal arsenates with the copper impregnated in the carbon. Arsenic is also adsorbed independently of the impregnated copper. Ion pair formation has been ruled out in the latter case. Arsenic desorption is easily achieved using strong acidic or alkaline solutions. When acidic solutions are used, copper is also eluted.

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.

Electrochemical interactions between gold and its associated minerals during cyanidation

Gold occurs in the Witwatersrand gold mines in association with sulphide, oxide and gangue minerals. The leaching behaviour of gold in contact or in association with various minerals depends largely on the galvanic interaction between gold and the mineral, and partially on the formation of a passivating film on the gold surface. Gold in contact with conducting minerals will passivate as a result of the enhanced magnitude of the cathodic current, resulting in decreased gold dissolution rates. n nChalcopyrite, pyrite and pyrrhotite cause the largest decrease in the rate of leaching when in contact with gold. Galena strongly enhances the dissolution rate owing to the action of dissolve Pb2+ ions on the surface of the gold. When these Pb2+ ions are prevented from coming into contact with the gold, galena inhibits the leaching rate. In all experiments the rotating disc of gold passivated so that the rate of dissolution was much slower than that predicted by a mass-transport limiting model. n nThe various films that occur on the surface of the gold and associated minerals, as well as the galvanic interaction (related to electrical conductivity), depend largely on the pretreatment of the ore. Pre-elimination of host minerals from the gold-bearing ore has a marked positive effect on the dissolution rate of gold, and explains the kinetics of reaction on the gold surface to a large extent. From the results presented in this paper on passivation owing to both film formation and galvanic interaction, it can be seen that passivation is not merely a laboratory curiosity, but it can reduce the efficiency of industrial operations and is thus most relevant to practical leaching processes.

The application of neural nets in the metallurgical industry

Abstract Although the potential of new techniques for the construction of accurate plant models, such as those based on connectionist methods, is generally acknowledged, little on their practical application can be found in the chemical and metallurgical engineering literature. In this paper the use of neural nets to model gold losses on a reduction plant and the consumption of an additive on a leach plant, as well as the pyrometallurgical processing of zinc and aluminium is discussed. The gold and leach plant models performed better than the multilinear regression models used on the plants, even where relatively few data were available. The neural networks used to model the recovery of lead and zinc from industrial flue dusts, process synthesis of zinc recovery plants and the processing of secondary aluminium in a rotary salt flux furnace produced realistic results that could be used by plant personnel to optimize their operations.

The leaching characteristics of Ni–Cu matte in the acid–oxygen pressure leach process at Impala Platinum

Abstract A study has been carried out to elucidate the leaching mechanism of Ni–Cu matte in an acid–oxygen (CuSO 4 –H 2 SO 4 –O 2 ) pressure leach process at Impala Platinum Refineries. This process is based on the initial Sherritt Gordon process at a temperature of 140°C and a pressure of 550 kPa. The complex interaction of the various synthetic minerals with one another in the leaching process has been clarified and indications found that certain crystallographic rules exist for the various synthetic mineral species to leach. Nickel and copper are leached from the sulphide lattice to form nickel and copper sulphides with decreasing Ni to S and Cu to S ratios, i.e., Ni 3 S 2 –Ni 7 S 6 –NiS–Ni 3 S 4 and Cu 2 S–Cu 31 S 16 –Cu 1.8 S–CuS, respectively. The kinetics for the cementation of copper from the leach liquor in the initial stages are very fast, as are the kinetics for the leaching of Ni alloy from the matte. The leaching of Ni alloy creates a porous structure in the matte particle to improve the leaching efficiency of the nickel and copper sulphides. H 2 S was detected during the experiment (even with an O 2 partial pressure of 180 kPa) which inhibited the leaching kinetics and led to the formation of a quasi-intermediate nickel sulphide (Ni 7 S 6 ) and copper sulphide (Cu 31 S 16 ) product. The key to the selective leaching of the nickel from the Ni–Cu matte is that while leaching Ni 3 S 2 , copper ions in solution are continuously precipitated as Cu 2 S in a substitution reaction liberating nickel ions into solution. Therefore, nickel is initially leached selectively from the Ni–Cu matte due to the presence of Ni 3 S 2 in the solids. The presence of heazlewoodite (Ni 3 S 2 ) is the factor controlling the leaching of copper simultaneously with nickel instead of the effect of the oxidising leach and followed by the non-oxidising leach as it is currently postulated in the literature.

The use of linear programming in the optimal design of flotation circuits incorporating regrind mills

Abstract Two linear models, the second being a subset of the first, are proposed for the simulation of flotation plants by use of linear programming. The first linear model produces the circuit structure, as well as the optimal flow rates of the valuable element between any number of flotation banks incorporating any number of recycle mills. An optimal grade for the valuable element in the concentrate is given by the second model. Operating conditions in the flotation banks and recycle mills are included as bounds in these models, permitting their possible application in expert systems. The simulated circuit structure, concentrate grade and recoveries closely resemble those of similar industrial flotation plants. The only data required by the simulation models are the feed rates of the species of an element, and their separation factors which are estimated from a multiparameter flotation model.