Factor-contributing Classification Of Magnetic Separation And Flotation And Application On The Unit Grade Curve Model Basis

Abstract:It has been accepted that simulation Technology has the inherent advantage of prediction and optimization of beneficiation processing.Reliability of simulation results overly depends on the availability of mathematical models. The work in this thesis foucused on researching recovery-yield model on the unit grade curve basis, proposed firstly by Fan Zhao-lin in Research on Mathematical Model of Flotation Indexes, furtherly. It was aimed to explore the availability of this model and lay reliable foundation for subsequent simulation and optimization.This work consists of threefolds. Firstly, recovery-yield model of double distribution unit grade curve was derived on the basis of single distribution unit grade curve. And formulae of unit grade curve are riched to match more data. More importantly, the classification principles of factors-affecting flotation was extended to general sorting process. Namely, the change of first class factor could enable to the partten of recovery-yield curve, on the other hand, second class factor only got concentrates fluctuat along the recovery-yield curve.Secondly, Statistical Methods are introduced into this research creatively. Classification criteria of both magnetic and flotation contributing factors were proposed respectively through taking advantage of data derived form relevant references. In terms of magnetic separartion, the first class factors were feed size. In contrast, the second class factors were field force, feed density, rinsing water. With reference to floatation, the first class factor werefeed size, pH modifers, activators and depressants, while the second class factor are dispersants, feed density, flotation time and collectors.The application of recovery-yield curve model was prediction and assessment. Specifically, it could be used to predict the experimental data, caused by the second factors, at the same time, for condition test of the first factors, the model would be made to assessment. Addtionnaly, an example, combining a sulfur oxide copper beneficiation process was given to demonstrate the feasibility of the model used to predict or assess.