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您的检索式：作者名="R.Venugopal"
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| 1 | Performance evaluation for selectivity of the flocculant on hematite in selective flocculation显示文摘Increased demand for iron ore necessitates the utilization of low-grade iron ore fines,slimes,and existing tailings.Selective flocculation can be an alternative physico-chemical process for utilizing these low-grade fines,slimes,and tailings.In selective flocculation,the most critical objective is the selection of proper reagents that will make floc of desired minerals.In present study,selective flocculation was applied to ultra-fine synthetic mixtures of hematite and kaolinite,and the Fe value was upgraded up to 65.78% with the reduction of Al2O3 and SiO2 values to 2.65% and 3.66%,respectively.Here,degraded wheat starch was used as a flocculant.In this process,separation occurs on the basis of the selectivity of the flocculant.The selectivity of the flocculant can be quantified in terms of separation effciency.Here,an attempt was also made to develop a correlation between separation effciency and major operating parameters such as flocculent dose,pH value,and solid concentration to predict the separation performance. | Lopamudra Panda Pradip K.Banerjee Surendra Kumar Biswal R.Venugopal N.R.Mandre | 2013 | International Journal of Minerals,Metallurgy and Materials2013，20，12： | 4 |
| 2 | Artificial neural network approach to assess selective flocculation on hematite and kaolinite显示文摘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 Panda P.K.Banerjee Surendra Kumar Biswal R.Venugopal N.R.Mandre | 2014 | International Journal of Minerals,Metallurgy and Materials2014，21，7： | 1 |
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