Flotation Kinetics. II. The Effect of Size on the Behavior of Galena Particles.

Gaudin, A. M.; Schuhmann, Rainer; Schlechten, A. W.

doi:10.1021/j150422a013

Cited by 80 publications

(15 citation statements)

References 1 publication

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“…Aggregation among coal particles has been reported previously to occur in a flotation cell (Gaudin et al, 1942;Morris, 1952;. The degree of aggregation increases as a function of hydrophobicity of the coal particles and the amount of the oily collector present in the system.…”

Section: Aggregation In the Coal Flotation Pulpmentioning

confidence: 79%

Physical and chemical interactions in coal flotation

Polat

Chander

2003

International Journal of Mineral Processing

189

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Coal flotation is a complex process involving several phases (particles, oil droplets and air bubbles). These phases simultaneously interact with each other and with other species such as the molecules of a promoting reagent and dissolved ions in water. The physical and chemical interactions determine the outcome of the flotation process. Physical and chemical interactions between fine coal particles could lead to aggregation, especially for high rank coals. Non-selective particle aggregation could be said to be the main reason for the selectivity problems in coal flotation. It should be addressed by physical (conditioning) or chemical (promoters) pretreatment before or during flotation. Although the interactions between the oil droplets and coal particles are actually favored, stabilization of the oil droplets by small amounts of fine hydrophobic particles may lead to a decrease in selectivity and an increase in oil consumption. These problems could be remedied by use of promoters that modify the coal surface for suitable particle -particle, droplet -particle and particle -bubble contact while emulsifying the oil droplets. The role of promoters may be different for different types of coals, however. They could be employed as modifiers to increase the hydrophobicity of low rank coals whereas their main role might be emulsification and aggregation control for high rank coals. In this paper, a detailed description of the various phases in coal flotation, their physical and chemical interactions with each other in the flotation pulp, the major parameters that affect these interactions and how these interactions, in turn, influence the flotation process are discussed. D

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Section: Aggregation In the Coal Flotation Pulpmentioning

confidence: 79%

Physical and chemical interactions in coal flotation

Polat

Chander

2003

International Journal of Mineral Processing

189

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“…One of the key parameters in flotation is the particle size distribution of the feed and it has been shown in several studies that the optimal size range for flotation is relatively narrow, approximately 20 -150 µm (Gaudin et al, 1942;Jameson et al, 2007). Particle hydrophobicity and contact angle (in addition to hydrodynamic factors) determine the attachment of particles to bubbles in the pulp, which in turn affects the froth stability (Klassen and Mokrousov, 1963;Johansson and Pugh, 1992).There remains much debate, however, on the effect of particle size on froth stability.…”

Section: Introductionmentioning

confidence: 99%

The effect of particle size distribution on froth stability in flotation

Norori-McCormac

Brito-Parada

Hadler

et al. 2017

Separation and Purification Technology

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Separation of particles of different surface properties using froth flotation is a widely-used industrial process, particularly in the minerals industry where it is used to concentrate minerals from ore. One of the key challenges in developing models to predict flotation performance is the interdependent nature of the process variables and operating parameters, which limits the application of optimising process control strategies at industrial scale. Froth stability, which can be quantified using air recovery (the fraction of air entering a flotation cell that overflows in the concentrate as unburst bubbles), has been shown to be linked to flotation separation performance, with stable froths yielding improved mineral recoveries. While it is widely acknowledged that there is an optimum particle size range for collection of particles in the pulp phase, the role of particle size on the measured air recovery and the resulting link to changes in flotation performance is less well understood. This is related to the difficulty in separating particle size and liberation effects.In this work, the effects of particle size distribution on air recovery are studied in a single species (silica) system using a continuous steady-state laboratory flotation cell. This allows an investigation into the effects of particle size distribution only on froth stability, using solids content and solids recovery as indicators of flotation performance. It is shown that, as the cell air rate is increased, the air recovery of the silica system passes through a peak, exhibiting the same froth behaviour as measured industrially. The air recovery profiles of systems with three different particle size distributions (d80 of 89.6, 103.5 and 157.1 m) are compared. The results show that, at lower air rates, the intermediate particle size distribution (103.5 m) yields the most stable froth, while at higher air rates, the finest particles (89.6 m) result in higher air recoveries. This is subsequently linked to changes in flotation performance. The results presented here highlight, for the first time, the link between particle size distribution in flotation feeds, air recovery and flotation performance. The results demonstrate that there is an optimal air rate for each particle size distribution, therefore changes in particle size distribution in the feed to flotation cells require a change in air rate in order to maximise mineral recovery.

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“…Neither a flotation rate representation (e.g., an f (k) in any rate domain) nor the relationship of the model parameters with the fast-and slow-floating components have been reported in flotation. The Laplace inversion of [1 − R(t = s)/R ∞ ] was again used to represent this model from first-order f (k)s. Two conditions must be taken into consideration: (i) for a RR ≤ 1, the argument of the Inverse Laplace transform exp −(k RR • t) a RR corresponds to a stretched exponential, which allows first-order f (k)s to be obtained from Equation (13) [72,73], (ii) for a RR > 1, the argument of the Inverse Laplace transform corresponds to a compressed exponential, which cannot be represented by the sum of exponentials of Table 5 [72]. Thus, the compressed exponentials cannot be represented by first-nor any nth-order reaction.…”

Section: Rosin-rammler Model Represented As a Distributed First-order Modelmentioning

confidence: 99%

“…The Inverse Laplace transform of the stretched exponential (a RR ≤ 1) was obtained from numerical integration of Equation (13). The first-order f (k)s were crosschecked from the numerical Laplace inversion of [1 − R(t = s)/R ∞ ], using the methodology reported by Valsa and Brančik [64].…”

Section: Rosin-rammler Modelmentioning

confidence: 99%

“…Imaizumi and Inoue [5] and Woodburn and Loveday [9] extended the first-order models of Garcia-Zuñiga [1] and Schuhmann Jr [2] as continuous sum of exponentials to account for the wide range of recovery rates typically observed in flotation. The representation of the flotation process as a sum of rate constants has received more attention as the different flotation performances by size [10][11][12][13], by association [5,14], by liberation or composition [14][15][16][17] and others can be masked by a single probability density function. From the first-order generalizations reported by Imaizumi and Inoue [5] and Woodburn and Loveday [9] several distributions have been proposed to describe f (k), including Rectangular [18], Exponential (also named fully mixed reactor model) [19], Sinusoidal [20], Triangular [21], Normal [22], Double Normal [23] and Weibull [24], among others.…”

Section: Introductionmentioning

confidence: 99%

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Representation of Kinetics Models in Batch Flotation as Distributed First-Order Reactions

Vinnett

Waters

2020

Minerals

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Four kinetic models are studied as first-order reactions with flotation rate distribution f(k): (i) deterministic nth-order reaction, (ii) second-order with Rectangular f(k), (iii) Rosin–Rammler, and (iv) Fractional kinetics. These models are studied because they are considered as alternatives to the first-order reactions. The first-order representation leads to the same recovery R(t) as in the original domain. The first-order R∞-f(k) are obtained by inspection of the R(t) formulae or by inverse Laplace Transforms. The reaction orders of model (i) are related to the shape parameters of first-order Gamma f(k)s. Higher reaction orders imply rate concentrations at k ≈ 0 in the first-order domain. Model (ii) shows reverse J-shaped first-order f(k)s. Model (iii) under stretched exponentials presents mounded first-order f(k)s, whereas model (iv) with derivative orders lower than 1 shows from reverse J-shaped to mounded first-order f(k)s. Kinetic descriptions that lead to the same R(t) cannot be differentiated between each other. However, the first-order f(k)s can be studied in a comparable domain.

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Flotation Kinetics. II. The Effect of Size on the Behavior of Galena Particles.

Cited by 80 publications

References 1 publication

Physical and chemical interactions in coal flotation

Physical and chemical interactions in coal flotation

The effect of particle size distribution on froth stability in flotation

Representation of Kinetics Models in Batch Flotation as Distributed First-Order Reactions

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