Jacques Eksteen

A mathematical modelling study of fluid flow and mixing in full-scale gas-stirred ladles

A full-scale, three-dimensional, transient mathematical model for application to gas-stirred ladles was developed. Multiphase aspects were accounted for by employing the Lagrangian Discrete Phase Model (DPM) in describing the bubble plume and the Eulerian Volume of Fluid (VOF) model for tracking the free surface of the melt. The standard k?e (SKE) model was used for modelling turbulence. Further research is required to refine the turbulence modelling approach, but validation experiments showed that the present approach yielded accurate information on bulk fluid flow and mixing in the ladle. The resulting model is easily generalised and computationally efficient.

An integrated thermochemical-systems approach to the prediction of matte composition dynamics in an Ausmelt® nickel–copper matte converter☆

Abstract The Ausmelt® converter is being introduced into the South African platinum industry as an alternative to the traditional Pierce Smith converter process to convert a nickel–copper matte containing platinum group metals (PGMs). The semi-fundamental modelling technique used involved developing a complete thermochemical equilibrium database of the system, and then using this in conjunction with linear systems models to predict the dynamic iron and sulphur concentrations in the matte. The thermochemical equilibrium database was developed using the FACT thermochemical simulation software. The generated equilibrium database was used to train and validate feedforward neural networks. This technique allowed the database to be used online to predict the matte equilibrium composition. The equilibrium matte concentrations of iron and sulphur predicted by the neural network were then used as exogenous inputs into two linear systems models used to predict the actual iron and sulphur matte concentrations. It was found that the models characterised the dynamic behaviour of the sulphur and iron well.

Surfactant-aided coal dust suppression: A review of evaluation methods and influencing factors

There is an increasing trend in the occurrence of coal workers pneumoconiosis even in developed countries such as the US and Australia who have believed such an issue have been well controlled in the past. Water spray is one of the most commonly applied methods for underground coal mines dust control, and research have shown the dust suppression efficiency can be greatly improved by adding surfactants. However, the literature appears to show inconsistent results that do not provide the coal mining industry with a clearly effective solution. The breakthrough in this field relies on the achievements in prior work, but an up-to-date critical review was not found. By critically reviewing prior studies, this paper highlights the advances in the surfactant-aided coal dust suppression technology. Firstly, the surfactant chemical structure, surfactant type and mechanism of surfactant adsorption were explained. Secondly, the commonly used surfactant efficiency evaluation methods were described. This is important for producing comparable and reproducible results. After that, key aspects of the influencing factors were discussed, which are essential for developing effective and robust dust suppression products. In the discussion on the challenges and further research directions, we suggest more focus should be on the dynamic interaction between the coal particle and water droplet in wind tunnels or well controlled onsite conditions.

Removal of arsenic from gold processing circuits by use of novel magnetic nanoparticles

ABSTRACT Arsenic adversely affects gold mining operations by interfering with the extraction of gold, as well as posing a significant health and environmental hazard. While a number of technologies are available for removing arsenic, none of them is effective under all conditions. Although adsorption is a promising approach, most methods focus on the purification of water under neutral or acidic conditions and tend to be less effective in gold mining process waters, operating under highly alkaline conditions. In this study, the removal of As(III) and As(V) from both arsenic-only solutions and simulated process waters using composite magnetic nanoparticles was investigated. The nanoparticles consisted of magnetite (Fe3O4) or maghemite (γ-Fe2O3) cores covered by various metal oxides with Langmuir adsorption capacities of As(III) and As(V) ranging from 31.4 to 79.1 mg g−1 and 10.2 to 25.5 mg g−1, respectively, in arsenic-only solutions at pH 9. The adsorption capacities were further characterised by adsorption tests conducted in simulated process waters. The ability to remove As(III) is of particular importance as it is harder to remove it from alkaline solutions than As(V). The magnetic cores allow simple and efficient magnetic recovery of the As-loaded nanoparticles.

Leaching of rare earths from fine-grained zirconosilicate ore

Abstract Leaching of rare earths Y, La and Ce by sulphuric acid from fine-grained zirconosilicate ore was investigated using Taguchi method of experimental design. An orthogonal array of L 8 , 2 7 which denotes 7 factors at 2 levels was chosen to consider the various factors relevant to the leaching process: baking time, baking temperature, acid dosage, leaching time, leaching temperature, grind size and dilution. Statistical analysis showed that sulphation baking was a significant step for the leaching of rare earths from the whole-of-ore and optimized leaching of rare earths involved the following condition: baking for 3 h at 320 °C at 3.2 g acid/g ore acid dosage followed by water leaching at 20 °C for 1 h and dilution of 20 mL water/g ore using 300 um grind size. The effect of each leaching factor was also discussed.

The Effect of Granulated Nickel Converter Matte Mineralogy on Phase-Specific Hardness and Associated Breakage Characteristics

There is very little in-depth study on the processing behavior of granulated nickel converter matte as an intermediate product, particularly related to downstream comminution and hydrometallurgical processing. The measurement of fundamental phase-specific physical properties would assist in understanding grinding and leaching processing behavior. The aim of this study was to investigate a possible dependence between phase-specific hardness, microstructures, and breakage characteristics. Relevant to the investigation is the application of a novel combination of micro-analytical techniques. The phase-specific hardness and breakage characteristics were sequentially tested with a CSM Nano-Indentation Tester. Phase-breakage characteristics were further tested in a laboratory ball mill at a specific energy input. Comparative phase-breakage analysis was additionally performed using a field-emission scanning electron microscope. It was found that the softest phase is copper sulfide with an average hardness of 1975 MPa. The harder phases are NiCu-alloy and nickel sulfide with average hardness values of 4981 MPa and 4456 MPa, respectively. Indentation-induced micro-cracks are common along curving copper-sulfide phase boundaries while there is a notable smaller degree of breakage attainable with respect to the harder nickel-sulfide phases. In addition, there is a comparative lack of breakage attainable with respect to NiCu-alloy phases, suggesting possible ductility under both the compressive indentation loads and during grinding events in a laboratory ball mill.

On-line prediction of transient phenomena in the slag phase during ilmenite smelting

Abstract Slag foaming and metal entrainment in high titania slags are complex dynamic phenomena that bears strong non-linear relationships to the slag chemistry and furnace operating conditions. These phenomena are traditionally difficult to predict and control on a feedforward basis. Feedback controls tend to be inefficient due to the large process time constants of industrial furnaces. A system identification technique lends itself to a novel approach for process analysis, when it is used in conjunction with fundamental process and thermodynamic knowledge. This paper presents the results of non-linear, dynamic modelling of transient phenomena such as the chemical causes of slag foaming and entrainment during the carbothermic reduction of ilmenite in open-arc smelting furnaces. The system is characterised using non-linear system identification techniques such as backpropagation neural networks. It will be shown how the outputs of the system characterisation techniques in combination with knowledge of the physico-chemical fundamentals can used in decision support on a smelter plant. Consequently, feedforward control strategies can be developed to minimise unwanted behaviour. The propensity of slags to foam is a non-linear function of slag viscosity, surface and interfacial tensions, slag density, reaction kinetics and slag inventory, which, in turn, are strongly nonlinear functions of the slag chemistry, the type and amount of solid phases present in the slag and the furnace operating conditions. The same factors also influence metal entrainment in the slag phase and consequently metal recovery. This research aims to shed some light on predicting when solid precipitation will take place that may eventually cause severe slag foaming during the carbothermic open-arc smelting of ilmenite.