South Africa is the world’s largest producer of platinum group elements (PGEs). Mining takes place in the Bushveld Complex, and recent statistics, (Mudd, 2010), showed that the UG2 reef is the main source of production, accounting for approximately 60% of world mining production. However, recovery by flotation is complicated by variations in the mineral composition, the need to grind fine and entrainment of chromite, which has an adverse effect on the subsequent smelting of the concentrate. The recovery of PGEs is variable, and it is influenced by PGE feed properties such as degree of liberation, mineral type and grain size. Conventional rougher batch flotation tests on drill core samples do not provide sufficient information for predicting plant performance. The aim of this research was to develop a rigorous method for the testing of UG2 drill core samples. A rougher-cleaner flotation test procedure was developed, and statistical tests were applied to select an appropriate model, which included entrainment of hydrophilic minerals. Fifty UG2 samples from across the Bushveld were milled at a fixed energy input, and the new test procedure was applied to derive model parameters for all samples. There was a significant variability in the PGE recovery, and typical feed characteristics such as PGE feed grade and grind did not show a clear link to the PGE recovery. This was due to the complex mineralogy of the PGE minerals and variations in ore hardness. Hence, a statistical modelling algorithm was used to determine the factors affecting PGE recovery, and an empirical model was developed, which relates the PGE recovery to feed properties. The model can be used to estimate PGE recovery based on feed properties. Samples which had a high base metal content (e.g. high nickel to iron ratio) had a high PGE recovery, and samples which were altered (e.g. high Rb/Sr ratio and loss on ignition) had a low PGE recovery. Depressant addition is used in PGE flotation to control the recovery of gangue, but it also affects the flotation of composite PGE/gangue particles. Seven of the fifty UG2 samples were selected for a more detailed investigation, using a more advanced batch flotation test and a mineralogical liberation analysis. The advanced batch flotation test was a new development, in which flotation model parameters were derived simultaneously for flotation after two stages of grinding and a combined cleaning stage. The effect of a range of depressant additions was also modelled. The floatable PGE fraction, determined from batch modelling, was linked to the mineralogical liberation analysis of the feed. The model is the first of its kind, and it makes it possible to predict the mineralogical characteristics of the feed from flotation data. A spread-sheet simulator was developed, to demonstrate how batch data (from the advanced flotation test) could be used to predict plant performance. Scale-up parameters were derived by using pilot-plant data for one of the ores. The spread-sheet was then used to optimise the plant design and depressant addition for an ore, while constraining, the mass of concentrate and the chromite content. The example showed that there was an optimum depressant addition and rougher-cleaner volume capacity for an ore. The gambit of this study was the linking of feed chemical assay and mineralogical properties to PGE recovery. The application of mineralogical tests and modelling of data from the advanced flotation test has demonstrated that the link is relatively complex. / Thesis (Ph.D.)-University of KwaZulu-Natal, Durban, 2013.

No description available.

Thesis (PhDApSc(MineralsandMaterials))--University of South Australia, 2001.

Ore floatability is defined as the propensity of particles to float within a flotation environment and any effective mathematical model of the flotation process must incorporate its effect. The objective of this thesis was to review the ore floatability models in the literature and for those considered appropriate: • investigate their basic assumptions, • evaluate the type of experimental data required to derive model parameters, • and test their predictive capability. A review of the literature identified three different methods of representing ore floatability in flotation circuit models. Two approaches were studied within this thesis, namely the empirically derived floatability component model and the property based floatability component model. The third shaped distribution modelling approach was considered too inflexible a methodology to represent all types of ore floatability distributions. Ore floatability cannot be directly measured but must be inferred from a flotation response. In this thesis, it was investigated using batch laboratory flotation tests which, for a particular system, were all performed using the same set of operating conditions. Any difference in response between tests performed in this way was attributed to a change in ore floatability. Within this thesis, hundreds of batch laboratory flotation tests were performed using samples collected from the streams of seven different industrial flotation circuits. These tests, in combination with circuit survey data, were used to investigate various assumptions underpinning the ore floatability models. The tests also provide the experimental information required to derive the flotation properties of the two chosen ore floatability models. Both the two models investigated in this thesis assume the floatability of a particle in a flotation circuit to remain the same before and after processes in the circuit. A nodal analysis technique was developed by the author to compare the floatability in the feed and product of a flotation circuit process using batch laboratory flotation test information. This technique was used to show that ore floatability is a conserved property across most flotation, mixing and cycloning processes. In the cases where floatability was not conserved, it is suspected that the surfaces of the particles have changed due to oxidation, adsorption of hydrophilic species or decomposition of reagent surface species. Ore floatability was usually not conserved across processes which are designed to change particle properties (e.g. reagent addition and regrinding). An additional requirement of the ore floatability models is that all particles assigned to a particular component must float with a similar flotation rate. Sizing and liberation analysis of batch laboratory flotation test data showed that property based floatability component models based on size and liberation alone do not satisfy this criteria. It was concluded that a property based model would need to include information about the chemical state of the particle surfaces. As this type of measurement was considered beyond the scope of this thesis, no further analysis of this type of model was possible. Techniques for determining the empirically derived floatability component model parameters were studied using statistical techniques. This analysis showed that unique, stable parameters could be obtained by fitting the models to match multiple batch flotation test data collected at the same time as a circuit survey. It was found that a minimum of four batch laboratory flotation tests were required to derive statistically stable parameters. The use of one laboratory batch flotation test (the traditional method of parameter derivation) results in parameters which are highly sensitive to the error in the batch laboratory flotation test data. A methodology to simulate and predict ore grade and recovery in a flotation circuit based on different ore floatability particle groupings was developed by the author. A theoretical analysis was performed using this algorithm which showed that a two floating component and one non-floating component model produced similar predictions to a multi-component model developed using size and liberation information in a circuit subject to changes in cell operation, residence time and circuit configuration. It was therefore concluded that a discrete floatability component model has the ability to represent, what is in reality, a more complex particle floatability distribution. The analysis performed in this thesis shows that the empirically derived ore floatability component model is a valid method of representing ore floatability within a flotation circuit model which does not contain grinding or reagent addition processes. Parameters of the model can be derived with statistical confidence using multiple batch flotation test data. To effectively model ore floatability in circuits containing regrinding or staged reagent addition, ore floatability models need to be developed which incorporate parameters related to the physical properties of the ore. It is therefore recommended that research be performed to determine the effect of size, liberation and chemical conditioning on the ore floatability of a particle and how these effects are best incorporated into an ore floatability model.

No description available.

No description available.

Flotation models describe the separability of mineral particles using a loosely defined term known as floatability or probability of collection, a response of particles in the pulp zone of a flotation vessel, unrelated to events in the froth phase. The Bubble Load measurement samples the particles that have attached to bubbles in the pulp zone and, thus, should give an indication of floatability. This thesis investigates the role of floatability in flotation models, and assesses the Bubble Load measurement as a direct estimate of floatability. Towards these ends, continuous pilot-scale flotation tests were carried out in which collector addition rate was varied. This allowed the floatability of mineral particles to be back-calculated on a size-by-liberation basis and be compared to the measured Bubble Load. Contrary to expectations it was found that, with increasing collector addition rate, the “floatability” of a majority of galena particle classes did not increase after 5 mg/kg of collector, while the measured Bubble Load actually decreased. This was found to be due to the stability of the froth increasing with collector addition rate, causing more galena to reach the concentrate, and less to drop-back to the pulp phase to be reattached. Conversely, there were other particle classes (such as sphalerite and coarse galena) where the expected trends were found, namely increasing floatability and Bubble Load with collector addition rate. These results indicate a high level of interdependence between the pulp and froth zones of the studied flotation cell through the flow of material between them (internal reflux). In the case of galena, the flow of material returning via drop-back from the froth had a greater impact on the Bubble Load than the galena in the feed made sufficiently hydrophobic to attach to bubbles. This means that, for the system studied here, the Bubble Load measurement does not provide a direct estimate of mineral particle floatability, defined as a sole consequence of pulp phase events. Moreover, for the current case, it appears that this definition of floatability is not sufficient to capture the interactions between the pulp and the froth. It is recommended that pulp and froth zone flotation models be developed in concert, recognising the interaction between the two zones, and that flotation models be formulated with due allowance for the material transport paths within a flotation vessel. It should be recognised that “floatability” is an aid for the imagination; a term for a process, rather than a particle “property” to be measured. An extension of the kinetic chemical reaction analogue, incorporating flotation sub-processes, is suggested/revived, to give some phenomenological basis to kinetic flotation models.

Thesis (Ph.D.)(Metallurgical Engineering)--University of Pretoria, 2005. / Includes summary. Includes bibliographical references.

Techn. Universiẗat, Diss., 2002--Freiberg (Sachsen).

Thesis (Ph. D.)--West Virginia University, 2007. / Title from document title page. Document formatted into pages; contains xvii, 189 p. : ill. (some col.). Vita. Includes abstract. Includes bibliographical references (p. 150-162).