Computational modelling of bubbles, droplets and particles in metals reduction and refining

The CFD modelling of metals reduction processes particularly always seems to involve the interaction of liquid metals, a gas (often air) top space, liquid droplets in the top space and injection of both solid particles and gaseous bubbles into the bath. These phases all interact and exhange mass, momentum and energy. Often it is the extent to which these multi-phase phemomena can be effectively captured within the CFD model which determines whether or not a tool of genuine use to the target industry sector can constructed. In this paper we discuss these issues in the context of two problems - one involving the injection of sparging gases into a steel continuous caster and the other based on the development of a novel process for aluminium electrolysis.

Computational modelling of bubbles, droplets and particles in metals reduction and refining

A multi-phase framework is typically required for the CFD modelling of metals reduction processes. Such processes typically involve the interaction of liquid metals, a gas (often air) top space, liquid droplets in the top space and injection of both solid particles and gaseous bubbles into the bath. The exchange of mass, momentum and energy between the phases is fundamental to these processes. Multi-phase algorithms are complex and can be unreliable in terms of either or both convergence behaviour or in the extent to which the physics is captured. In this contribution, we discuss these multi-phase flow issues and describe an example of each of the main “single phase” approaches to modelling this class of problems (i.e., Eulerian–Lagrangian and Eulerian–Eulerian). Their utility is illustrated in the context of two problems – one involving the injection of sparging gases into a steel continuous slab caster and the other based on the development of a novel process for aluminium electrolysis. In the steel caster, the coupling of the Lagrangian tracking of the gas phase with the continuum enables the simulation of the transient motion of the metal–flux interface. The model of the electrolysis process employs a novel method for the calculation of slip velocities of oxygen bubbles, resulting from the dissolution of alumina, which allows the efficiency of the process to be predicted.

Modelling the mixing of lead bullion during the refining process

Abstract not available

The relationship between disgust sensitivity, anxiety and obsessions

Three hundred participants, including volunteers from an obsessional support group, filled in questionnaires relating to disgust sensitivity, health anxiety, anxiety, fear of death, fear of contamination and obsessionality as part of an investigation into the involvement of disgust sensitivity in types of obsessions. Overall, the data supported the hypothesis that a relationship does exist between disgust sensitivity and the targeted variables. A significant predictive relationship was found between disgust sensitivity and total scores on the obsessive compulsive inventory (OCI; Psychological Assessment 10 (1998) 206) for both frequency and distress of symptomatology. Disgust sensitivity scores were significantly related to health anxiety scores and general anxiety scores and to all the obsessional subscales, with the exception of hoarding. Additionally, multiple regression analyses revealed that disgust sensitivity may be more specifically related to washing compulsions: frequency of washing behaviour was best predicted by disgust sensitivity scores. Washing distress scores were best predicted by health anxiety scores, though disgust sensitivity entered in the second model. It is suggested that further research on the relationship between disgust sensitivity and obsessionality could be helpful in refining the theoretical understanding of obsessions.

Application of CFD in vacuum dezincing process

Most lead bullion is refined by pyrometallurgical methods - this involves a serics of processes that remove the antimony (softening) silver (Parkes process), zinc (vacuum dezincing) and if need be, bismuth (Betterton-Kroll process). The first step, softening, removes the antimony, arsenic and tin by air oxidation in a furnace or by the Harris process. Next, in the Parkes process, zinc is added to the melt to remove the silver and gold. Insoluble zinc, silver and gold compounds are skimmed off from the melt surface. Excess zinc added during desilvering is removed from lead bullion using one of ghree methods: * Vacuum dezincing; * Chlorine dezincing; or * Harris dezincing. The present study concentrates on the Vacuum dezincing process for lead refining. The main aims of the research are to develop mathematical model(s), using Computational Fluid Dyanmics (CFD) a Surface Averaged Model (SAM), to predict the process behaviour under various operating conditions, thus providing detailed information of the process - insight into its reaction to changes of key operating parameters. Finally, the model will be used to optimise the process in terms of initial feed concentration, temperature, vacuum height cooling rate, etc.

Computational modelling-a key component in materials processing

Computational analysis software is now widely accepted as a key industrial tool for plant design and process analysis. This is due in part to increased accuracy in the models, larger and faster computer systems and better graphical interfaces that allow easy use of the technology by engineers. The use of computational modelling to test new ideas and analyse current processes helps to take the guesswork out of industrial process design and offers attractive cost savings. An overview of computer-based modelling techniques as applied to the materials processing industry is presented and examples of their application are provided in the contexts of the mixing and refining of lead bullion and the manufacture of lead ingots.