Artificial neural network approach to assess selective flocculation on hematite and kaolinite

作     者：Lopamudra Panda P.K.Banerjee Surendra Kumar Biswal R.Venugopal N.R.Mandre 

作者机构：R&D Tata Steel Limited Institute of Minerals and Materials Technology(Council of Scientific and Industrial Research) Department of Fuel & Minerals EngineeringIndian School of Mines University 

出 版 物：《International Journal of Minerals,Metallurgy and Materials》 (矿物冶金与材料学报（英文版）)

年 卷 期：2014年第21卷第7期

页      面：637-646页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081902[工学-矿物加工工程] 0819[工学-矿业工程] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：the funding given by Council of Scientific and Industrial Research(CSIR) India through project NWP-31 for this project 

主　　题：hematite kaolinite flocculation artificial neural networks back propagation algorithm Fourier transform infrared spectroscopy separation efficiency 

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 ad-sorption 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.