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

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

References

[1] Rao DS, Kumar TVV, Rao SS, Prabhakar S, Raju GB. Mineralogy and geochemistry of a low grade iron ore sample from bellary-hospet sector, India and their implications on beneficiation. J. Miner. Mater. Charact. Eng., 2009, 8(2): 115

[2] Singh R, Rath R, Nayak B, Bhattacharya K. Development of process for beneficiation of low-grade iron ore samples from Orissa, India. Proceedings of XXV International Mineral Processing Congress (IMPC), Brisbane, Australia, 2010 1235

[3] Das B, Prakash S, Mohapatra BK, Bhaumik SK, Narasimha KS. Beneficiation of iron ore slimes using hydrocyclone. Miner. Metall. Process, 1992 101

[4] Roy S, Das A. Characterization and processing of low-grade iron ore slime from the Jilling area of India. Miner. Process. Extract. Metall. Rev., 2008, 29(3): 213.

[5] Panda L, Biswal SK, Tathavadkar V. Beneficiation of synthetic iron ore kaolinite mixture using selective flocculation. J. Miner. Mater. Charact. Eng., 2010, 9(11): 973

[6] Panda L, Biswal SK. Selective flocculation of synthetic iron ores-kaolinite mixtures. Int. Seminar on Min. Process. Technol., 2009, Bhubaneswar, IMMT

[7] Panda L, Biswal SK, Banerjee PK, Venugopal R, Mandre NR. Performance evaluation for selectivity of the flocculant on hematite in selective flocculation. Int. J. Miner. Metall. Mater., 2013, 20(12): 1123.

[8] Villar JW, Dawe GA. The Tilden mine: a new processing technique for iron ore. Min. Congr. J., 1975, 61, 40

[9] Weissenborn PK, Warren LJ, Dunn JG. Optimisation of selective flocculation of ultrafine iron ore. Int. J. Miner. Process., 1994, 42(3–4): 191.

[10] Weissenborn PK, Warren LJ, Dunn JG. Selective flocculation of ultrafine iron ore: 1. Mechanism of adsorption of starch onto hematite. Colloid. Surf. A, 1995, 99(1): 11.

[11] Attia YA. Development of a selective flocculation process for a complex copper ore. Int. J. Miner. Process., 1977, 4(3): 209.

[12] Bagster DF, McIlvenny JD. Studies in the selective flocculation of hematite from gangue using high molecular weight polymers. Part 1: chemical factors. Int. J. Miner. Process., 1985, 14(1): 1.

[13] Beklioglu B, Arol AI. Selective flocculation behavior of chromite and serpentine. Physicochem. Probl. Miner. Process., 2004, 38, 103

[14] Drzymala J, Fuerstenau DW. Selective flocculation of hematite in the hematite-quartz-ferric ion-polyacrylic acid system. Part 1, activation and deactivation of quartz. Int. J. Miner. Process., 1981, 8(3): 265.

[15] Abro MI. Up Gradation Of Dilband Iron Ore, 2009, Jamshoro, Mehran University of Engineering and Technology

[16] Jin RR, Hu WB, Hou XJ. Mechanism of selective flocculation of hematite quartz with hydrolyzed polyacrylamide. Colloids Surf., 1987, 26, 317.

[17] Wills BA. Mineral Processing Technology: An Introduction to the Practical Aspects of the Treatment and Mineral Recovery, 1997 6th Ed. 19

[18] Johnston CT, Sposito G, Birge RR. Raman spectroscopic study of kaolinite in aqueous suspension. Clays Clay Miner., 1985, 33(6): 483.

[19] W. Reusch, Infrared Spectroscopy. http://www2.chemistry.msu.edu/faculty/reusch/VirtTxtJml/Spectrpy/InfraRed/infrared.htm.

[20] Characteristic IR Absorption Frequencies of Organic Functional Groups [2013-12-29]. http://www2.ups.edu/faculty/hanson/Spectroscopy/IR/IRfrequencies.html

[21] Frost RL, Xi YF, Scholz R. A vibrational spectroscopic study of the phosphate mineral cyrilovite Na(Fe³⁺)₃(PO₄)₂(OH)₄·2(H₂O) and in comparison with wardite. Spectrochim. Acta Part A, 2013, 108, 244.

[22] Infrared Spectroscopy [2013-11-01]. http://en.wikipedia.org/wiki/Infrared_spectroscopy.

[23] Kar B, Sahoo H, Rath SS, Das B. Investigations on different starches as depressants for iron ore flotation. Miner. Eng., 2013, 49, 1.

[24] Cutmore NG, Liu Y, Middleton AG. Ore characterization and sorting. Miner. Eng., 1997, 10, 421.

[25] Jorjani E, Chelgani SC, Mesroghli Sh. Application of artificial neural networks to predict chemical desulfurization of Tabas coal. Fuel, 2008, 87(12): 2727.

[26] Kalyani VK, Pallavika Chaudhuri S, Charan TG, Haldar DD, Kamal KP, Badhe YP, Tambe SS, Kulkarni BD. Study of a laboratory-scale froth flotation process using artificial neural networks. Miner. Process. Extract. Metall. Rev., 2008, 29, 130.

[27] Kalyani VK, Charan TG, Haldar DD, Sinha A, Suresh N. Coal-fine beneficiation studies of a bench-scale water-only cyclone using artificial neural network. Int. J. Coal Prep. Utiliz., 2008, 28(2): 94.

[28] Panda L, Sahoo AK, Tripathy A, Biswal SK, Sahu AK. Application of artificial neural network to study the performance of jig for beneficiation of non-coking coal. Fuel, 2012, 97, 151.

[29] Moghadassi A, Parvizian F, Hosseini SM. A new approach based on artificial neural networks for prediction of high pressure vapour-liquid equilibrium. Aust. J. Basic Appl. Sci., 2009, 3, 1851

[30] Sahoo A, Roy GK. Artificial neural network approach to segregation characteristic of binary homogeneous mixtures in promoted gas-solid fluidized beds. Powder Technol., 2007, 171(1): 54.

[31] Jha GK. Published Report on Introduction to Artificial Neural Network, 2005, New Delhi, Indian Agricultural Research Institute, 1

[32] Jain AK, Mao JC, Mohiuddin KM. Artificial neural networks: a tutorial. Computer, 1996, 29(3): 31.