Statistical modeling of copper losses in the silicate slag of the sulfide concentrate smelting process

• Authors: Marija V. Savic , Predrag B. Djordjevic , Ivan N. Mihajlovic , Zivan D. Zivkovic
• Languages of publication: EN

Abstracts

EN

This article presents the results of the statistical modeling of copper losses in the silicate slag of the sulfide concentrates smelting process. The aim of this study was to define the correlation dependence of the degree of copper losses in the silicate slag on the following parameters of technological processes: SiO2, FeO, Fe3O4, CaO and Al2O3 content in the slag and copper content in the matte. Multiple linear regression analysis (MLRA), artificial neural networks (ANNs) and adaptive network based fuzzy inference system (ANFIS) were used as tools for mathematical analysis of the indicated problem. The best correlation coefficient (R2 = 0.719) of the final model was obtained using the ANFIS modeling approach.

Keywords

EN

modeling; copper concentrate; smelting process; copper losses; slag; ANFIS

• Publisher: De Gruyter Open
• Journal: Polish Journal of Chemical Technology
• Year: 2015
• Volume: 17
• Issue: 3
• Pages: 62-69

Dates

published

1 - 9 - 2015

online

19 - 9 - 2015

Contributors

author

Marija V. Savic

• University of Belgrade, Technical Faculty in Bor Vojske Jugoslavije 12, 19210 Bor, Serbia

author

Predrag B. Djordjevic

• University of Belgrade, Technical Faculty in Bor Vojske Jugoslavije 12, 19210 Bor, Serbia

author

Ivan N. Mihajlovic

• University of Belgrade, Technical Faculty in Bor Vojske Jugoslavije 12, 19210 Bor, Serbia

author

Zivan D. Zivkovic

• University of Belgrade, Technical Faculty in Bor Vojske Jugoslavije 12, 19210 Bor, Serbia

References

• 1. Schlesinger, M.E., King, M.J. & Sole, K.C. & Davenport W.G. (2011). Extractive Metallurgy of Copper (5th ed.). Oxford, UK: Elsevier.
• 2. Fernandez-Caliani, J.C., Rios, G., Martinez, J. & Jimenez, F.J. (2012). Occurrence and speciation of copper in slags obtained during the pyrometallurgical processing of chalcopyrite concentrates at the Huelva smelter (Spain). J. Min. Metall., Sect. B, 48(2), 161–171. DOI: 10.2298/jmmb111111027f.[Crossref][WoS]
• 3. Sarrafi, A., Rahmati, B., Hassani, H.R. & Shirazi, H.H.A. (2004). Recovery of copper from reverberatory furnace slag by flotation. Miner. Eng., 17, 457–459. DOI: 10.1016/j.mineng.2003.10.018.[Crossref]
• 4. Moskalyk, R.R. & Alfantazi, A.M. (2003). Review of copper pyrometallurgical practice: today and tomorrow. Miner. Eng., 16, 893–919. DOI: 10.1016/j.mineng.2003.08.002.[Crossref]
• 5. Shi, C., Meyer, C. & Behnood, A. (2008). Utilization of copper slag in cement and concrete. Resour., Conserv. Recycl., 52, 11151120. DOI: 10.1016/j.resconrec.2008.06.008.[Crossref][WoS]
• 6. Gorai, B., Jana, R.K. & Premchand, P. (2003). Characteristics and utilisation of copper slag – a review. Resour., Conserv. Recycl., 39, 299–313. DOI: 10.1016/S0921-3449(02)00171-4.[Crossref]
• 7. Jalkanen, H., Vehviläinen, J. & Poijärvi, J. (2003). Copper in solidified copper smelter slags. Scand. J. Metall., 32, 65–70. DOI: 10.1034/j.1600-0692.2003.00536.x.[Crossref]
• 8. Zivkovic, Z., Mitevska, N., Mihajlovic, I. & Nikolic, Dj. (2010). Copper losses in sulfide concentrate smelting slag are dependent on slag composition. Miner. Metall. Process., 27, 141–147.
• 9. Acuna, C. & Sherrington, M. (2005). Slag cleaning processes: A growing concern. Mater. Sci. Forum., 475, 2745–2752. DOI: .[Crossref]
• 10. Živković, Ž., Mitevska, N., Mihajlović, I. & Nikolić, Đ. (2009). The influence of the silicate slag composition on copper losses during smelting of the sulfide concentrates. J. Min. Metall., Sect. B, 45, 23–34. DOI: 10.2298/JMMB0901023Z.[WoS][Crossref]
• 11. Goni, C. & Sanchez, M. (2009). Modeling of copper content variation during „El Teniente“ slag cleaning process. VIII International Conference on Molten Slags, Fluxes & Salts. Santiago, Chile, 123–131.
• 12. Djordjevic, P., Mitevska, N., Mihajlovic, I., Nikolić, Dj., Manasijevic, D. & Zivkovic, Z. (2012). The effect of copper content in the matte on the distribution coefficients between the slag and the matte for certain elements in the sulphide copper concentrate smelting process. J. Min. Metall., Sect. B, 48, 143–151. DOI: 10.2298/JMMB111115012D.[WoS][Crossref]
• 13. Djordjevic, P., Mitevska, N., Mihajlovic, I., Nikolic, Dj. & Zivkovic, Z. (2014). Effect of the slag basicity on the coefficient of distribution between copper matte and the slag for certain metals. Miner. Process. Extr. Metall. Rev., 35, 202–207. DOI: 10.1080/08827508.2012.738731.[WoS][Crossref]
• 14. Mitevska, N., Živković, Ž. & Marinković, J. (2000). The influence of reverb slag composition on copper losses. J. Min. Metall., Sect. B, 36, 63–76.
• 15. Sridhar, R., Toguri, J.M., Simeonov, S. (1997). Copper losses and thermodynamic considerations in copper smelting. Metall. Mater. Trans. B, 28, 191–200. DOI: 10.1007/s11663-997-0084-5.[Crossref]
• 16. Li u, J., Gui, W., Xie, Y. & Yang, C. (2014). Dynamic modeling of copper flash smelting process at a Smelter in China. Appl. Math. Model., 38(7–8), 2206–2213. DOI: .[Crossref]
• 17. Liu, J., Gui, W., Xie, Y. & Jiang, Z. (2013). Solving the Transient Cost-Related Optimization Problem for Copper Flash Smelting Process with Legendre Pseudospectral Method. Mater. Trans., 54(3), 350–356. DO I: 10.2320/matertrans.M2012350.[WoS]
• 18. Živković, Ž., Mihajlović I. & Nikolić Đ. (2009). Artificial neural network method applied on the nonlinear multivariate problems. Serb. J. Manag., 4, 143–155.
• 19. Živković, Ž., Mihajlović, I., Djurić, I. & Štrbac, N. (2010). Statistical modeling of the industrial sodium aluminate solutions decomposition process. Metall. Mater. Trans. B, 41, 1116–1122. DOI: 10.1007/s11663-010-9407-z.[Crossref]
• 20. Kalina, J. (2014). On robust information extraction from high-dimensional data. Serb. J. Manag., 9(1), 131–144. DOI: 10.5937/sjm9-5520.[Crossref]
• 21. Azlan Hussain, M. (1999). Review of the applications of neural networks in chemical control – simulation and online implementation. Artif. Intell. Eng., 13, 55–68. DOI: 1016/S0954-1810(98)00011-9.
• 22. Bloch, G. & Denoeux, T. (2003). Neural networks for process control and optimization: two industrial applications. ISA Trans., 42, 39–51. DOI: 10.1016/S0019-0578(07)60112-8.[Crossref]
• 23. Chehreh Chelgani, S. & Jorjani, E. (2009). Artificial neural network prediction of Al2O3 leaching recovery in the Bayer process – Jajarm alumina plant (Iran). Hydrometallurgy, 97, 105–110. DOI: 10.1016/j.hydromet.2009.01.008.[WoS][Crossref]
• 24. Jang, J.S.R. (1993). ANFIS: Adaptive-network based fuzzy inference system. IEEE Trans. Syst., Man, Cybern., Syst., 23, 665–685. DOI: 10.1109/21.256541.[Crossref]
• 25. Savic, M., Mihajlovic I. & Zivkovic, Z. (2013). An ANFIS-based air quality model for prediction of SO2 concentration in urban area. Serb. J. Manag., 8, 25–38. DOI: 10.2139/ssrn.2257533.[Crossref]
• 26. Karami, A. & Afiuni-Zadeh, S. (2012). Sizing of rock fragmentation modeling due to bench blasting using adaptive neuro-fuzzy inference system and radial basis function. Int. J. Min. Sci. Technol., 22, 459–463. DOI: 10.1016/j.ijmst.2012.06.001.[Crossref]
• 27. Han, Y., Zeng, W., Zhang, X., Zhao, Y., Sun, Y. & Ma, X. (2013). Modeling the relationship between hydrogen content and mechanical property of Ti600 alloy by using ANFIS. Appl. Math. Model., 37, 5705–5714. DOI: 10.1016/j.apm.2012.11.008.[WoS][Crossref]
• 28. Fragiadakis, N.G., Tsoukalas, V.D. & Papazoglou, V.J. (2014). An adaptive neuro-fuzzy inference system (ANFIS) model for assesing occupational risk in the shipbuilding industry. Safety Sci., 63, 226–235. DOI: 1016/j.ssci.2013.11.013.[WoS]
• 29. Mihajlović, I., Đurić, I. & Živković, Ž. (2014). ANFIS based prediction of the aluminum extraction from boehmite bauxite in the Bayer process. Pol. J. Chem. Tech., 16(1),103–109. DOI: 10.2478/pjct-2014-0018.[Crossref]
• 30. Moroney, R.N. (1998). Spurious of virtual correlation errors commonly encountered in reduction of scientific data. J. Wind. Eng. Ind. Aerod., 77&78, 543–553.[Crossref]
• 31. Demuth, H. & Beale, M. (2002). Neural Network Toolbox for Use with MATLAB, Handbook. The MathWorks Inc., Natick, MA.
• 32. Jang, J.S.R., Sun, C.T. & Mizutani, E. (1997). Neuro-Fuzzy and Soft Computing – A Computational Approach to Learning and Machine Intelligence. Prentice Hall, Cambridge, MA.
• 33. Subashini, L. & Vasudeven M. (2012). Adaptive neuro-fuzzy inference system (ANFIS) – based models for predicting the weld bead width and depth of penetration from the infrared thermal image of the weld pool. Metall. Mater. Trans. B, 43, 145–154. DOI: 10.1007/s11663-011-9570-x.[Crossref][WoS]
• 34. Takagi, T. & Sugeno, M. (1985). Fuzzy identification systems and its application to modeling and control. IEEE Trans. Syst., Man, Cybern., Syst., 15, 116–132. DOI: 10.1109/tsmc.1985.6313399.[Crossref]
• 35. MATLAB, V.7.1 (2007). The MathWorks Inc., Natick, MA.

Identifiers

DOI

10.1515/pjct-2015-0051