Experimental investigation and adaptive neural fuzzy inference system prediction of copper recovery from flotation tailings by acid leaching in a batch agitated tank

Jalil Pazhoohan; Hossein Beiki; Morteza Esfandyari

doi:10.1007/s12613-019-1762-4

Volume 26 Issue 5

May 2019

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Article Navigation > International Journal of Minerals, Metallurgy and Materials > 2019 > 26(5): 538-546

Jalil Pazhoohan, Hossein Beiki, and Morteza Esfandyari, Experimental investigation and adaptive neural fuzzy inference system prediction of copper recovery from flotation tailings by acid leaching in a batch agitated tank, Int. J. Miner. Metall. Mater., 26(2019), No. 5, pp. 538-546. https://doi.org/10.1007/s12613-019-1762-4

Cite this article as:

Jalil Pazhoohan, Hossein Beiki, and Morteza Esfandyari, Experimental investigation and adaptive neural fuzzy inference system prediction of copper recovery from flotation tailings by acid leaching in a batch agitated tank, Int. J. Miner. Metall. Mater., 26(2019), No. 5, pp. 538-546. https://doi.org/10.1007/s12613-019-1762-4

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Research Article

Experimental investigation and adaptive neural fuzzy inference system prediction of copper recovery from flotation tailings by acid leaching in a batch agitated tank

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Corresponding author:
Hossein Beiki E-mail: hbeiki@qiet.ac.ir
Received: 17 May 2018; Revised: 4 November 2018; Accepted: 13 November 2018

Abstract

Abstract

The potential of copper recovery from flotation tailings was experimentally investigated using a laboratory-mixing tank. The experiments were performed with solid weight percentages of 30wt%, 35wt%, 40wt% and 45wt% in water. The measurements revealed that adding sulfuric acid all at once to the tank rapidly increased the efficiency of the leaching process, which was attributed to the rapid change in the acid concentration. The rate of iron dissolution from tailings was less than when the acid was added gradually. The sample with 40wt% solid is recommended as an appropriate feed for the recovery of copper. The adaptive neural fuzzy system (ANFIS) was also used to predict the copper recovery from flotation tailings. The back-propagation algorithm and least squares method were applied for the training of ANFIS. The validation data was also applied to evaluate the performance of these models. Simulation results revealed that the testing results from these models were in good agreement with the experimental data.
- flotation tailings;
- leaching;
- copper;
- environments;
- adaptive neural fuzzy inference system

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[1]	T.Y. Gao, K.R. Liu, Q. Han, and B.S. Xu, Enrichment of copper and recycling of cyanide from copper-cyanide waste by solvent extraction, Int. J. Miner. Metall. Mater., 23(2016), No. 11, p. 1258.
[2]	Y. Jia, H.Y. Sun, Q.Y. Tan, H.S. Gao, X.L. Feng, and R.M. Ruan, Linking leach chemistry and microbiology of low-grade copper ore bioleaching at different temperatures, Int. J. Miner. Metall. Mater., 25(2018), No. 3, p. 271.
[3]	O.I. Nkwachukwu, C.H. Chima, A.O. Ikenna, and L. Albert, Focus on potential environmental issues on plastic world towards a sustainable plastic recycling in developing countries, Int. J. Ind. Chem., 4(2013), No. 1, p. 34.
[4]	A. Aslan, A. Güneş, E. Salur,Ö.S.Şahin, H.B. Karadağ, and A. Akdemir, Mechanical properties and microstructure of composites produced by recycling metal chips, Int. J. Miner. Metall. Mater., 25(2018), No. 9, p. 1070.
[5]	W.P. Liu and X.F. Yin, Recovery of copper from copper slag using a microbial fuel cell and characterization of its electrogenesis, Int. J. Miner. Metall. Mater., 24(2017), No. 6, p. 621.
[6]	T. Chen, C. Lei, B. Yan, and X.M. Xiao, Metal recovery from the copper sulfide tailing with leaching and fractional precipitation technology, Hydrometallurgy, 147(2014), p. 178.
[7]	W.J. Li, S. Liu, Y.S. Song, J.K. Wen, G.Y. Zhou, and Y. Chen, Comprehensive recovery of gold and base-metal sulfide minerals from a low-grade refractory ore, Int. J. Miner. Metall. Mater., 23(2016), No. 12, p. 1377.
[8]	J.V. Oliveira, T. Duarte, J.C. Costa, A.J. Cavaleiro, M.A. Pereira, and M.M. Alves, Improvement of biomethane production from sewage sludge in Co-digestion with glycerol and waste frying oil, using a design of experiments, BioEnergy Res., 11(2018), No. 4, p. 763.
[9]	M. Keramati and H. Beiki, The effect of pH adjustment together with different substrate to inoculum ratios on biogas production from sugar beet wastes in an anaerobic digester, J. Energy Manage. Technol., 1(2017), No. 2, p. 6.
[10]	J.N. Lie, M.B. Rizkiana, F.E. Soetaredjo, Y.H. Ju, and S. Ismadji, Production of biodiesel from sea mango (Cerbera odollam) seed using in situ subcritical methanol-water under a non-catalytic process, Int. J. Ind. Chem., 9(2018), No. 1, p. 53.
[11]	H. Beiki and M. Keramati, Improvement of methane production from sugar beet wastes using TiO₂ and Fe₃O₄ nanoparticles and chitosan micropowder additives, Appl. Biochem. Biotechnol., 2019. https://doi.org/10.1007/s12010-019-02987-2.
[12]	P.J. Landrigan, R. Fuller, N.J.R. Acosta, et al., The lancet commission on pollution and health, The Lancet, 139(2018), No. 10119, p. 462.
[13]	M.S. Li, Y.P. Luo, and Z.Y. Su, Heavy metal concentrations in soils and plant accumulation in a restored manganese mineland in Guangxi, South China, Environ. Pollut., 147(2007), No. 1, p. 168.
[14]	J.L. Schnoor, Environmental Modeling:Fate and Transport of Pollutants in Water, Air, and Soil, John Wiley and Sons Inc., New York, 1996.
[15]	M. Erfanmanesh and M. Afyuni, Environmental Pollution (Water, Soil and Air), Arkan Publication, Isfahan, 2013.
[16]	W.Y. Zheng, Y.W. Zhou, H. Gu, and Z.P. Tian, Seasonal dynamics and impact factors of urban forest CO₂ concentration in Harbin, China, J. For. Res., 28(2017), No. 1, p. 125.
[17]	Z.Y. Ma, H.Y. Yang, S.T. Huang, Y. Lü, and L. Xiong, Ultra fast microwave-assisted leaching for the recovery of copper and tellurium from copper anode slime, Int. J. Miner. Metall. Mater., 22(2015), No. 6, p. 582.
[18]	H.K. Hansen, J.B. Yianatos, and L.M. Ottosen, Speciation and leachability of copper in mine tailings from porphyry copper mining:influence of particle size, Chemosphere, 60(2005), No. 10, p. 1497.
[19]	S.B. Noei, S. Sheibani, F. Rashchi, and S.M.J. Mirazimi, Kinetic modeling of copper bioleaching from low-grade ore from the Shahrbabak Copper Complex, Int. J. Miner. Metall.Mater., 24(2017), No. 6, p. 611.
[20]	M. Gericke, A. Pinches, and J.V. Van Rooyen, Bioleaching of a chalcopyrite concentrate using an extremely thermophilic culture, Int. J. Miner. Process., 62(2001), No. 1-4, p. 243.
[21]	I.M. Ahmed, A.A. Nayl, and J.A. Daoud, Leaching and recovery of zinc and copper from brass slag by sulfuric acid, J. Saudi Chem. Soc., 20(2016), No. S1, p. S280.
[22]	M.M. Antonijević and G. Bogdanović, Investigation of the leaching of chalcopyritic ore in acidic solutions, Hydrometallurgy, 73(2004), No. 3-4, p. 245.
[23]	M.M. Antonijević, M.D. Dimitrijević, Z.O. Stevanović, S.M. Serbula, and G.D. Bogdanovic, Investigation of the possibility of copper recovery from the flotation tailings by acid leaching, J. Hazard. Mater., 158(2008), No. 1, p. 23.
[24]	J.A. Muñoz, D.B. Dreisinger, W.C. Cooper, and S.K. Young, Silver-catalyzed bioleaching of low-grade copper ores. Part Ⅱ:Stirred tank tests, Hydrometallurgy, 88(2007), No. 1-4, p. 19.
[25]	J.H. Canterford, P.T. Davey, and G. Tsambourakis, The influence of ferric iron on the dissolution of copper from lump oxide ore:Implications in solution mining, Hydrometallurgy, 15(1985), No. 1, p. 93.
[26]	H. Salehi, S. Zeinali-Heris, M. Esfandyari, and M. Koolivand, Nero-fuzzy modeling of the convection heat transfer coefficient for the nanofluid, Heat Mass Transfer, 49(2013), No. 4, p. 575.