APPLICATION OF MULTI CRITERIA DECISION MAKING METHODS TO LEACHING PROCESS OF COPPER FROM MALACHITE ORE

Yıl 2018, Cilt: 36 Sayı: 3, 783 - 794, 01.09.2018

Bilal Kurşuncu Ali Yaraş Hasan Arslanoğlu

Öz

This study concerns with optimization of the leaching conditions of copper from malachite ore in the presence ammonium nitrate solution. In order to select the effective parameter from available dissolution process, all experiments were performed using L25 orthogonal experimental design by Taguchi method and the temperature was found as a more effective parameter. Then multi-criteria decision making (MCDM) method was applied to experimental results and it was found that the process is in concordance with MCDM values. Based on the results, the highest copper extraction value (99.4%) was reached under optimum leaching conditions are as follows: time, 60 min; temperature, 75oC; stirring speed, 450 rpm; concentration of leaching reagent, 4 mol/L and solid/liquid ratio, 8 g/mL. In conclusion, Taguchi and MCDM method can be used effectively for optimization of various hydrometallurgical processes.

Anahtar Kelimeler

Leaching, malachite, ammonium nitrate, optimization, Taguchi, TOPSIS, MCDM

Kaynakça

• [1] Akcil, A., A preliminary research on acid pressure leaching of pyritic copper ore in Kure Copper Mine, Turkey. Minerals Engineering, 2002. 15(12): p. 1193-1197.
• [2] Arzutug, M.E., M.M. Kocakerim, and M. Copur, Leaching of Malachite Ore in NH3-Saturated Water. Industrial & Engineering Chemistry Research, 2004. 43(15): p. 4118-4123.
• [3] Ekmekyapar, A., R. Oya, and A. Künkül, Dissolution kinetics of an oxidized copper ore in ammonium chloride solution. Chemical and biochemical engineering quarterly, 2003. 17(4): p. 261-266.
• [4] Ekmekyapar, A., et al., Dissolution kinetics of an oxidized copper ore in water saturated by chlorine. Journal of Chemical Technology and Biotechnology, 1988. 43(3): p. 195-204.
• [5] Künkül, A., et al., Leaching kinetics of malachite in ammonia solutions. International journal of mineral processing, 1994. 41(3): p. 167-182.
• [6] Bingöl, D., M. Canbazoğlu, and S. Aydoğan, Dissolution kinetics of malachite in ammonia/ammonium carbonate leaching. Hydrometallurgy, 2005. 76(1–2): p. 55-62.
• [7] Oudenne, P. and F. Olson, Leaching kinetics of malachite in ammonium carbonate solutions. Metallurgical Transactions B, 1983. 14(1): p. 33-40.
• [8] Liu, W., et al., Dissolution kinetics of low grade complex copper ore in ammonia-ammonium chloride solution. Transactions of Nonferrous Metals Society of China, 2010. 20(5): p. 910-917.
• [9] Yartaşi, A. and M. Çopur, Dissolution kinetics of copper (II) oxide in ammonium chloride solutions. Minerals Engineering, 1996. 9(6): p. 693-698.
• [10] Thirumalai, R. and J.S. Senthilkumaar, Multi-criteria decision making in the selection of machining parameters for Inconel 718. Journal of Mechanical Science and Technology, 2013. 27(4): p. 1109-1116.
• [11] Dağdeviren, M., S. Yavuz, and N. Kılınç, Weapon selection using the AHP and TOPSIS methods under fuzzy environment. Expert Systems with Applications, 2009. 36(4): p. 8143-8151.
• [12] Chu, J. and Y. Su, The Application of TOPSIS Method in Selecting Fixed Seismic Shelter for Evacuation in Cities. Systems Engineering Procedia, 2012. 3(0): p. 391-397.
• [13] Baral, S.S., et al., Optimization of leaching parameters for the extraction of rare earth metal using decision making method. Hydrometallurgy, 2014. 143(0): p. 60-67.
• [14] Shanian, A. and O. Savadogo, TOPSIS multiple-criteria decision support analysis for material selection of metallic bipolar plates for polymer electrolyte fuel cell. Journal of Power Sources, 2006. 159(2): p. 1095-1104.
• [15] Jeya Girubha, R. and S. Vinodh, Application of fuzzy VIKOR and environmental impact analysis for material selection of an automotive component. Materials & Design, 2012. 37(0): p. 478-486.
• [16] Jahan, A., et al., A comprehensive VIKOR method for material selection. Materials & Design, 2011. 32(3): p. 1215-1221.
• [17] Shanian, A. and O. Savadogo, A material selection model based on the concept of multiple attribute decision making. Materials & Design, 2006. 27(4): p. 329-337.
• [18] Chatterjee, P. and S. Chakraborty, Material selection using preferential ranking methods. Materials & Design, 2012. 35(0): p. 384- 393.
• [19] Chatterjee, P., V.M. Athawale, and S. Chakraborty, Materials selection using complex proportional assessment and evaluation of mixed data methods. Materials & Design, 2011. 32(2): p. 851-860.
• [20] Maity, S.R., P. Chatterjee, and S. Chakraborty, Cutting tool material selection using grey complex proportional assessment method. Materials & Design, 2012. 36(0): p. 372-378.
• [21] Asiltürk, İ. and H. Akkuş, Determining the effect of cutting parameters on surface roughness in hard turning using the Taguchi method. Measurement, 2011. 44(9): p. 1697-1704.
• [22] Kıvak, T., Optimization of surface roughness and flank wear using the Taguchi method in milling of Hadfield steel with PVD and CVD coated inserts. Measurement, 2014. 50(0): p. 19-28.
• [23] Çalışkan, H., et al., Material selection for the tool holder working under hard milling conditions using different multi criteria decision making methods. Materials & Design, 2013. 45(0): p. 473-479.
• [24] Çalışkan, H., Selection of boron based tribological hard coatings using multi-criteria decision making methods. Materials & Design, 2013. 50(0): p. 742-749.
• [25] Tzeng, G.H. and J.J. Huang, Multiple Attribute Decision Making: Methods and Applications. 2011: Taylor & Francis.