Artificial neural network approach to assess selective flocculation on hematite and kaolinite |
| |
Authors: | Lopamudra Panda P. K. Banerjee Surendra Kumar Biswal R. Venugopal N. R. Mandre |
| |
Affiliation: | 1. R&D Tata Steel Limited, Jamshedpur, 831001, India 2. Institute of Minerals and Materials Technology (Council of Scientific and Industrial Research), Bhubaneswar, 751013, India 3. Department of Fuel & Minerals Engineering, Indian School of Mines University, Dhanbad, 826004, India
|
| |
Abstract: | 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. |
| |
Keywords: | hematite kaolinite flocculation artificial neural networks back propagation algorithm Fourier transform infrared spectroscopy separation efficiency |
本文献已被 维普 万方数据 SpringerLink 等数据库收录! |
| 点击此处可从《矿物冶金与材料学报》浏览原始摘要信息 |
|
点击此处可从《矿物冶金与材料学报》下载全文 |
|