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CURATOR
A pinboard by
Muhammad hayat

PhD candidate, Missouri university of science & Technology USA

PINBOARD SUMMARY

Statistical techniques will be used to optimize the flotation process.

Metals like lead ,copper, zinc and iron are very important for sustaining our modern life style.These metals are extracted form the metallic ores excavated from the earth.These metallic ores contain a lot of impurities.In order to obtain pure metals, these ores are processed.The first step of purifying these ores is the physical separation of metallic minerals from the non metallic minerals.Different methods are used for the physical separation.One such method is called froth flotation.In froth flotation bubbles are pumped into a tank containing slurry of the metallic ore.Hydrophobic metallic minerals go at the top of the tank with the bubbles while hydrophobic non metallic sink at the bottom of the tank, hence separation occurs. Different reagents needs to be added to the flotation tank to enhance this separation.These reagents are expensive and can effect the economics of the metal extraction process.It is therefore required to optimize the dosage of these reagents.In addition other factors of flotation like stirring rate of the slurry and air flow rate also effect the outcome of this process. This research is aimed to optimize all the parameters for sulphide mineral flotation process.Quadratic equations will be developed through experiments for the prediction of the flotation efficiency at different operating parameter levels.This research will generate models which can help flotation operators to run the plants in most economical and efficient way. This research will impact the lives of every human being who will be using metals in his life.

3 ITEMS PINNED

Grade-recovery modelling and optimization of the froth flotation process of a lepidolite ore

Abstract: With the increase in the demand for lithium, Li-bearing minerals could be considered as alternative resources to achieve the supplying. So, efficient technological solutions for the valorization of these minerals are required. In this context, the froth flotation process of a lepidolite ore was modelled and optimized. Closely following the response surface methodology (RSM), the effects of three independent process variables (pulp pH, flotation collector dosage and flotation time) upon two common measures of the separation (lithium recovery and lithium content) were studied. These were modelled using the experimental data obtained starting with the implementation and execution of a full 23 factorial design and ending with a (face-centered) central composite design (CCD), a second order design. The coefficients of the second-order polynomial regression models were fitted by solving linear least squares problems. After statistical validation, the fitted models were used to support the identification of the significant effects of the process variables and to provide estimations of the measures of separation (responses) for combinations of the levels of the process variables over a feasible region of interest. Using directly the measured values of Li recovery and Li content, the selected experimental Pareto optimal combination of the levels of the process variables is: pulp pH = 2, dosage of collector = 500 g.t− 1 and flotation time = 12 min producing a concentrate with Li recovery of 91.51% and a Li content of 1.96%. Using the fitted second order models for the separation criteria, a refined Pareto optimal combination was obtained as the solution of the multicriteria optimization (maximization) problem that was solved by different methods (Weighted Sum of Objectives, Goal Programming and Desirability Functions). The refined Pareto optimal combination was the same than the selected experimental Pareto optimal combination, only the collector dosage decreased to 470–478 g.t− 1, producing a concentrate with Li recovery around 92.50% and a Li content of 2.00%.

Pub.: 09 Nov '16, Pinned: 27 Jun '17

Kinetic modeling and optimization of flotation process in a cyclonic microbubble flotation column using composite central design methodology

Abstract: In this work, a composite central design with five levels and four variables was employed to model and optimize the batch flotation kinetic process in a cyclonic microbubble flotation column (FCMC). 30 sets of batch flotation rate tests were executed at different conditions of pulp concentration (X1), frother dosage (X2), flow rate of circulation pulp (X3) and froth depth (X4). It was observed the maximum flotation time (tmax) obtained in tests fluctuated wildly under different conditions. Statistical analysis based on the model fit and stability was performed to discriminate six kinetic models. The response surface methodology was used for the identification and development of significant relationship between process variables. Statistical analysis indicated that the modified Kelsall model was the optimal kinetic model for characterizing the flotation process. Analysis of variance results revealed that the effect of X1 was significant for all process responses. X4 was found as a significant independent factor for the two response variables of tmax and the ultimate combustible recovery (ε∞) of the optimal kinetic model. X3 had a significant influence on the parameter of the optimal kinetic model (the fraction of flotation components with the slow rate constant). Furthermore, the maximum flotation time and ε∞ were significantly influenced by the interaction between X1 and X4. Based on the result of optimization it was found that the desired ultimate combustible recovery with an appropriate flotation time was obtained from the flotation process with a given range of experimental variables (X1: from the intermediate levels to the higher levels; X2: the intermediate level; X3: 220 g/t and X4: 25.00 mm). There was an acceptable relationship between predicted and actual values with one of the optimal conditions (Adj. R2 = 0.9971). The response surface methodology was effective for predicting and optimizing the batch flotation process of FCMC.

Pub.: 09 Nov '16, Pinned: 27 Jun '17