Dense-medium cyclones have been used for beneficiation of fine particles of coal. In this study, the usability of cyclones in the beneficiation of tailings of a coal preparation plant was investigated. For this purpos...Dense-medium cyclones have been used for beneficiation of fine particles of coal. In this study, the usability of cyclones in the beneficiation of tailings of a coal preparation plant was investigated. For this purpose, separation tests were conducted using spiral concentrator and heavy medium cyclones with the specific weight of medium 1.3-1.8 （g/cm^3） on different grading fractions of tailing in an industrial scale （the weight of tail sample was five tons）. Spiral concentrator was utilized to beneficiate particles smaller than 1 mm. In order to evaluate the efficiency of cyclones, sink and float experiments using a specific weight of 1.3, 1.5, 1.7 and 1.9 g/cm^3, were conducted on a pilot scale. Based on the obtained results, the recovery of floated materials in cyclones with the specific weight of 1.40, 1.47 and 1.55 g/cm^3 are 17.75%, 33.80%, and 50%, respectively. Also, the cut point （Pso）, which is the relative density at which particles report equally to the both products are 1.40, 1.67 and 1.86 g/cm^3. The probable errors of separation for defined specific weights for cyclones are 0.080, 0.085 and 0.030, respectively. Also, the coefficients of variation was calculated to be 0.20, 0.12 and 0.03. Finally, it could be said that the performance of a cyclone with a heavy medium of 1.40 g/cm^3 specific weight is desirable compared with other specific weights.展开更多
In conventional chromite beneficiation plant, huge quantity of chromite is used to loss in the form of tailing. For recovery these valuable mineral, a gravity concentrator viz. wet shaking table was used.Optimisation ...In conventional chromite beneficiation plant, huge quantity of chromite is used to loss in the form of tailing. For recovery these valuable mineral, a gravity concentrator viz. wet shaking table was used.Optimisation along with performance prediction of the unit operation is necessary for efficient recovery.So, in this present study, an artificial neural network(ANN) modeling approach was attempted for predicting the performance of wet shaking table in terms of grade(%) and recovery(%). A three layer feed forward neural network(3:3–11–2:2) was developed by varying the major operating parameters such as wash water flow rate(L/min), deck tilt angle(degree) and slurry feed rate(L/h). The predicted value obtained by the neural network model shows excellent agreement with the experimental values.展开更多
文摘Dense-medium cyclones have been used for beneficiation of fine particles of coal. In this study, the usability of cyclones in the beneficiation of tailings of a coal preparation plant was investigated. For this purpose, separation tests were conducted using spiral concentrator and heavy medium cyclones with the specific weight of medium 1.3-1.8 （g/cm^3） on different grading fractions of tailing in an industrial scale （the weight of tail sample was five tons）. Spiral concentrator was utilized to beneficiate particles smaller than 1 mm. In order to evaluate the efficiency of cyclones, sink and float experiments using a specific weight of 1.3, 1.5, 1.7 and 1.9 g/cm^3, were conducted on a pilot scale. Based on the obtained results, the recovery of floated materials in cyclones with the specific weight of 1.40, 1.47 and 1.55 g/cm^3 are 17.75%, 33.80%, and 50%, respectively. Also, the cut point （Pso）, which is the relative density at which particles report equally to the both products are 1.40, 1.67 and 1.86 g/cm^3. The probable errors of separation for defined specific weights for cyclones are 0.080, 0.085 and 0.030, respectively. Also, the coefficients of variation was calculated to be 0.20, 0.12 and 0.03. Finally, it could be said that the performance of a cyclone with a heavy medium of 1.40 g/cm^3 specific weight is desirable compared with other specific weights.
文摘In conventional chromite beneficiation plant, huge quantity of chromite is used to loss in the form of tailing. For recovery these valuable mineral, a gravity concentrator viz. wet shaking table was used.Optimisation along with performance prediction of the unit operation is necessary for efficient recovery.So, in this present study, an artificial neural network(ANN) modeling approach was attempted for predicting the performance of wet shaking table in terms of grade(%) and recovery(%). A three layer feed forward neural network(3:3–11–2:2) was developed by varying the major operating parameters such as wash water flow rate(L/min), deck tilt angle(degree) and slurry feed rate(L/h). The predicted value obtained by the neural network model shows excellent agreement with the experimental values.
