Have a personal or library account? Click to login
Statistical modeling of copper losses in the silicate slag of the sulfide concentrate smelting process Cover

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

Open Access
|Sep 2015

References

  1. 1. Schlesinger, M.E., King, M.J. & Sole, K.C. & Davenport W.G. (2011). Extractive Metallurgy of Copper (5th ed.). Oxford, UK: Elsevier.
  2. 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.10.2298/JMMB111111027F
  3. 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.2003.10.018.10.1016/j.mineng.2003.10.018
  4. 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.2003.08.002.10.1016/j.mineng.2003.08.002
  5. 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.2008.06.008.10.1016/j.resconrec.2008.06.008
  6. 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)00171-4.10.1016/S0921-3449(02)00171-4
  7. 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.10.1034/j.1600-0692.2003.00536.x
  8. 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. 9. Acuna, C. & Sherrington, M. (2005). Slag cleaning processes: A growing concern. Mater. Sci. Forum., 475, 2745–2752. DOI: 10.4028/www.scientific.net/MSF.475-479.2745.10.4028/www.scientific.net/MSF.475-479.2745
  10. 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.10.2298/JMMB0901023Z
  11. 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. 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.10.2298/JMMB111115012D
  13. 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: 10.1080/08827508.2012.738731.10.1080/08827508.2012.738731
  14. 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. 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/s11663-997-0084-5.10.1007/s11663-997-0084-5
  16. 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: http://dx.doi.org/10.1016/j.apm.2013.10.035.
  17. 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.M2012350.10.2320/matertrans.M2012350
  18. 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. 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.10.1007/s11663-010-9407-z
  20. 20. Kalina, J. (2014). On robust information extraction from high-dimensional data. Serb. J. Manag., 9(1), 131–144. DOI: 10.5937/sjm9-5520.10.5937/sjm9-5520
  21. 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)00011-9.10.1016/S0954-1810(98)00011-9
  22. 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)60112-8.10.1016/S0019-0578(07)60112-8
  23. 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.2009.01.008.10.1016/j.hydromet.2009.01.008
  24. 24. Jang, J.S.R. (1993). ANFIS: Adaptive-network based fuzzy inference system. IEEE Trans. Syst., Man, Cybern., Syst., 23, 665–685. DOI: 10.1109/21.256541.10.1109/21.256541
  25. 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.2257533.10.2139/ssrn.2257533
  26. 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.2012.06.001.10.1016/j.ijmst.2012.06.001
  27. 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.2012.11.008.10.1016/j.apm.2012.11.008
  28. 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.2013.11.013.10.1016/j.ssci.2013.11.013
  29. 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-2014-0018.10.2478/pjct-2014-0018
  30. 30. Moroney, R.N. (1998). Spurious of virtual correlation errors commonly encountered in reduction of scientific data. J. Wind. Eng. Ind. Aerod., 77&78, 543–553.10.1016/S0167-6105(98)00171-8
  31. 31. Demuth, H. & Beale, M. (2002). Neural Network Toolbox for Use with MATLAB, Handbook. The MathWorks Inc., Natick, MA.
  32. 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.10.1109/TAC.1997.633847
  33. 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.10.1007/s11663-011-9570-x
  34. 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.1985.6313399.10.1109/TSMC.1985.6313399
  35. 35. MATLAB, V.7.1 (2007). The MathWorks Inc., Natick, MA.
Language: English
Page range: 62 - 69
Published on: Sep 19, 2015
Published by: West Pomeranian University of Technology, Szczecin
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2015 Marija V. Savic, Predrag B. Djordjevic, Ivan N. Mihajlovic, Zivan D. Zivkovic, published by West Pomeranian University of Technology, Szczecin
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.