Artificial neural network approach to assess selective flocculation on hematite and kaolinite
来源期刊：International Journal of Minerals Metallurgy and Materials2014年第7期
论文作者：Lopamudra Panda P.K.Banerjee Surendra Kumar Biswal R.Venugopal N.R.Mandre
文章页码：637 - 646
摘 要：Because of the current depletion of high grade reserves,beneficiation of low grade ore,tailings produced and tailings stored in tailing ponds is needed to fulfill the market demand.Selective flocculation is one alternative process that could be used for the beneficiation of ultra-fine material.This process has not been extensively used commercially because of its complex dependency on process parameters.In this paper,a selective flocculation process,using synthetic mixtures of hematite and kaolinite in different ratios,was attempted,and the adsorption mechanism was investigated by Fourier transform infrared（FTIR） spectroscopy.A three-layer artificial neural network（ANN）model（4-4-3） was used to predict the separation performance of the process in terms of grade,Fe recovery,and separation efficiency.The model values were in good agreement with experimental values.
Lopamudra Panda1，P.K.Banerjee1，Surendra Kumar Biswal2，R.Venugopal3，N.R.Mandre3
1. R&D Tata Steel Limited2. Institute of Minerals and Materials Technology(Council of Scientific and Industrial Research)3. Department of Fuel & Minerals Engineering,Indian School of Mines University
摘 要：Because of the current depletion of high grade reserves,beneficiation of low grade ore,tailings produced and tailings stored in tailing ponds is needed to fulfill the market demand.Selective flocculation is one alternative process that could be used for the beneficiation of ultra-fine material.This process has not been extensively used commercially because of its complex dependency on process parameters.In this paper,a selective flocculation process,using synthetic mixtures of hematite and kaolinite in different ratios,was attempted,and the adsorption mechanism was investigated by Fourier transform infrared（FTIR） spectroscopy.A three-layer artificial neural network（ANN）model（4-4-3） was used to predict the separation performance of the process in terms of grade,Fe recovery,and separation efficiency.The model values were in good agreement with experimental values.
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