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
In the present investigation, an attempt is made to examine the interdependencies among the operating parameters and their interactional effect on the separation performance of a water-only cyclone for treating ferrug...In the present investigation, an attempt is made to examine the interdependencies among the operating parameters and their interactional effect on the separation performance of a water-only cyclone for treating ferruginous chromite fines. Statistically designed experiments are carried out, and empirical models are developed for the critical response parameters, i,e., yield(%) to underflow, grade(%Cr_2O_3 and%SiO_2)and Cr:Fe ratio of the underflow stream. Further, using these empirical models, operating regime of the process parameters is optimized to obtain the peak performance of water-only cyclone. Also, efforts are made to validate the prediction models with the experimental results.展开更多
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.
文摘In the present investigation, an attempt is made to examine the interdependencies among the operating parameters and their interactional effect on the separation performance of a water-only cyclone for treating ferruginous chromite fines. Statistically designed experiments are carried out, and empirical models are developed for the critical response parameters, i,e., yield(%) to underflow, grade(%Cr_2O_3 and%SiO_2)and Cr:Fe ratio of the underflow stream. Further, using these empirical models, operating regime of the process parameters is optimized to obtain the peak performance of water-only cyclone. Also, efforts are made to validate the prediction models with the experimental results.
