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1

Kinetic modelling of flotation column and Jameson cell in coal

Karaca Sevgi, Ucar Ali, Sahbaz Oktay

Physicochemical Problems of Mineral Processing

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2022

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Vol. 58, iss. 5

art. no. 152848

EN

Physical enrichment technologies can be used worldwide in various coal washing plants to enrich up to 500 μm particle size. Conversely, coals smaller than this are discarded as waste, causing storage and environmental issues. In this regard, studies on coal below 500 μm in Turkey have recently acquired attraction. The Jameson flotation cell and flotation column, which have many uses worldwide but are not used throughout the plant in Turkey, were used to investigate the separation possibilities of coals below 500 μm. In the study, the flotation column and Jameson cell performances for three different particle sizes (-500+300, -300+212 and -212+106 μm) were compared. For the first time, both machines operated in a negative bias condition. In addition, the flotation kinetics of the machines were modelled with some critical operating parameters. Models illustrating the main and multiple effects of the parameters were developed using the data derived from the experimental results, and the models were statistically significant at the 95% confidence level. In the experiments performed with both flotation machines, the flotation rate increases with the decrease in particle size in general. According to the results, the velocity increase in the Jameson cell was 0.0050-0.0075 min-1 compared to the flotation column in the experiments performed in the size range of -500+300 μm, and the flotation rate constant increased approximately twice. In the size range of -212+106 μm, the difference became larger, and the flotation rate of the Jameson cell increased up to six times with a difference of 0.0450-0.0500 min-1.

2

Bubble loading profiles in a flotation column

Eskanlou A., Khalesi M. R., Abdollahy M.

Physicochemical Problems of Mineral Processing

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2018

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Vol. 54, iss. 2

355--362

EN

Bubble loading is the mass of hydrophobic particles attached per unit surface area of air. This measure can be used in the design and analysis of flotation columns as a sign of true flotation. To date, however, this measurement has been limited to the pulp-froth interface, which only indicates the maximum bubble loading and does not reflect the progress of the loading process. This paper introduces the concept of bubble loading profile that summarizes measures of bubble loading at different heights of the collection zone in a flotation column. The effects of bubble size, particle size and collector dosage on the introduced profiles are also investigated. These operational variables changed the bubble loading profile from a linear to a curved trend. The curvatures in the profiles were near the place of the feeding port and therefore the collection zone was divided into two separate zones in terms of bubble loading characteristics. The zone below the feeding port often did not contribute much to the loading of particles on the bubbles and the loading phenomenon mostly took place above the feeding port. Behaviors of the profiles in these two zones were analyzed to reveal that a change in the feeding port placement or column height can, under some conditions, increase the overall bubble loading and thus, ultimately, the true flotation.

3

Energy feature of a multi-flow column flotation process

Cheng G., Yu Y., Ma L., Xia W., Xu H.

Physicochemical Problems of Mineral Processing

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2017

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Vol. 53, iss. 2

1266--1284

EN

A cyclonic-static micro-bubble flotation column (FCSMC) has been widely used in mineral separation. FCSMC includes countercurrent, cyclone and jet flow mineralization zones in a single column. In this study, the energy feature of the three different zones was compared. The turbulent flow was evaluated in terms of the turbulent kinetic energy (k) and the turbulent dissipation rate (ε). An appropriate computing model was determined by comparing the flow field value measured by PIV with the results of the Fluent numerical simulation. Jet flow separation exhibited the maximum k and ε values among the three columns, whereas counter-current separation displayed the minimum values. The high circulating volumetric flowrate means great energy input and turbulent intensity. The higher turbulent dissipation rate, the smaller the bubble is. The better performance of the FCSMC was mainly attributed to the multiple mineralization steps. The floatability of mineral particles gradually decreases with an increase in flotation time, the mineralization energy gradually increased to overcome the decrease in mineral floatability. By contrast, the countercurrent was beneficial for recovering the coarse particles, and the jet flow was beneficial for recovering the fine particles.

4

Simultaneous optimization of flotation column performance using genetic evolutionary algorithm

Nakhaei F., Irannajad M., Yousefikhoshbakht M.

Physicochemical Problems of Mineral Processing

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2016

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Vol. 52, iss. 2

874--893

EN

Column flotation is a multivariable process. Its optimization guarantees the metallurgical yield of the process, expressed by the grade and recovery of the concentrate. The present work aimed at applying genetic algorithms (GAs) to optimize a pilot column flotation process which is characterized by being difficult to be optimized via conventional methods. A non-linear mathematical model was used to describe the dynamic behavior of the multivariable process. The solution of the optimization problem using conventional algorithms does not always lead to convergence because of the high dimensionality and non-linearity of the model. In order to deal with this process, the use of a genetic evolutionary algorithm is justified. In this way, GA was coupled with the multivariate non-linear regression (MNLR) of the column flotation metallurgical performance as a fitting function in order to optimize the column flotation process. Then, this kind of intelligent approach was verified by using mineral processing approaches such as Halbich’s upgrading curve. The aim of the optimization through GAs was searching for the process inputs that maximize the productivity of copper in the Sarcheshmeh pilot plant. In this case, the simulation optimization problem was defined as finding the best values for the froth height, chemical reagent dosage, wash water, air flow rate, air holdup, and Cu grade in rougher and column feed streams. The results indicated that GA was a robust and powerful search method to find the best values of the flotation column model parameters that lead to more reliable simulation predictions at a reasonable time. Based on the grade–recovery Halbich upgrading curve, the MNLR model coupled with GA can be used for determination of the flotation optimum conditions.

5

Influence of frother type and dose on collectorless flotation of copper-bearing shale in a flotation column

Kowalczuk P. B., Mroczko D., Drzymala J.

Physicochemical Problems of Mineral Processing

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2015

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Vol. 51, iss. 2

547--558

EN

In this paper the influence of nonionic (methyl isobutyl carbinol, tri(ethylene glycol) monobutyl ether) and cationic (hexylamine) frothers on flotation of copper-bearing shale in a flotation column was investigated. It was shown that naturally hydrophobic shale did not float in pure water but it floated in the presence of the investigated frothers. The real contact angle of shale, measured by the sessile drop method, was equal to about 40°, while its effective contact angle was zero when shale was floated in a flotation column in pure water. The investigated surfactants increased the effective hydrophobicity of shale from zero to 16±1, 22±1 and 33±2° for coarse, medium and fine particles, respectively. The calculations of the effective contact angle were based on a simplified probabilistic model of flotation.

6

Comparison between neural networks and multiple regression methods in metallurgical performance modeling of flotation column

Nakhaei F., Irannajad M.

Physicochemical Problems of Mineral Processing

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2013

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Vol. 49, iss. 1

255--266

EN

Artificial neural networks are relatively new computational tools which their inherent ability to learn and recognize highly non-linear and complex relationships makes them ideally suited in solving a wide range of complex real-world problems. In this research, different techniques (Linear regression, Non-linear regression, Back propagation neural network, Radial Basis Function for the estimation of Cu grade and recovery values in flotation column concentrate are studied. Modeling is performed based on 90 datasets at different operating conditions at Sarcheshmeh pilot plant, a copper concentrator in Iran, which include chemical reagents dosage, froth height, air and wash water flow rates, gas holdup and Cu grade in the rougher feed and flotation column feed, column tail and final concentrate streams. The results of models were also expressed and analyzed by intuitive graphics. The results indicated that a four-layer BP network gave the most accurate metallurgical performance prediction and all of the neural network models outperformed non-linear regression in the estimation process for the same set of data.