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
Locating the mineral processing plant near a mine is the most important parameter that affects the whole process.Many factors,and their preferences,should be considered in this stage.The factors include economical,geo...Locating the mineral processing plant near a mine is the most important parameter that affects the whole process.Many factors,and their preferences,should be considered in this stage.The factors include economical,geological,technical,environmental and tectonic parameters.A multi-criteria decision making method is necessary to rank the alternatives.In this paper we describe how plant location is selected by using the Analytic Hierarchy Process(AHP).This method,with eight criteria,was used to select a location for the mineral processing plant at the Sangan iron ore mine(phase 1).Three alternatives for the processing plant were evaluated.The main criteria were distance from the mine,access to heavy machinery transport,the amount of excavation required for grading,bed mixture capacity,belt conveyor length,distance from the tailing dam,distance from the waste dumps and surface water diversion requirements.Finally,the alternatives were ranked and the best location was proposed.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Locating the mineral processing plant near a mine is the most important parameter that affects the whole process.Many factors,and their preferences,should be considered in this stage.The factors include economical,geological,technical,environmental and tectonic parameters.A multi-criteria decision making method is necessary to rank the alternatives.In this paper we describe how plant location is selected by using the Analytic Hierarchy Process(AHP).This method,with eight criteria,was used to select a location for the mineral processing plant at the Sangan iron ore mine(phase 1).Three alternatives for the processing plant were evaluated.The main criteria were distance from the mine,access to heavy machinery transport,the amount of excavation required for grading,bed mixture capacity,belt conveyor length,distance from the tailing dam,distance from the waste dumps and surface water diversion requirements.Finally,the alternatives were ranked and the best location was proposed.
