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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

Green Sciences
Volume 17 (2015): Issue 3 (September 2015)
By: ,  ,   and    
Open Access
|Sep 2015
References

References

  1. 1. Schlesinger, M.E., King, M.J. & Sole, K.C. & Davenport W.G. (2011).(5th ed.). Oxford, UK: Elsevier.
    Search in Google Scholar
  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).48(2), 161–171. DOI: 10.2298/jmmb111111027f.
    Open DOISearch in Google Scholar
  3. 3. Sarrafi, A., Rahmati, B., Hassani, H.R. & Shirazi, H.H.A. (2004). Recovery of copper from reverberatory furnace slag by flotation.17, 457–459. DOI: 10.1016/j.mineng.2003.10.018.
    Open DOISearch in Google Scholar
  4. 4. Moskalyk, R.R. & Alfantazi, A.M. (2003). Review of copper pyrometallurgical practice: today and tomorrow.., 16, 893–919. DOI: 10.1016/j.mineng.2003.08.002.
    Open DOISearch in Google Scholar
  5. 5. Shi, C., Meyer, C. & Behnood, A. (2008). Utilization of copper slag in cement and concrete.., 52, 11151120. DOI: 10.1016/j.resconrec.2008.06.008.
    Open DOISearch in Google Scholar
  6. 6. Gorai, B., Jana, R.K. & Premchand, P. (2003). Characteristics and utilisation of copper slag – a review.., 39, 299–313. DOI: 10.1016/S0921-3449(02)00171-4.
    Open DOISearch in Google Scholar
  7. 7. Jalkanen, H., Vehviläinen, J. & Poijärvi, J. (2003). Copper in solidified copper smelter slags.32, 65–70. DOI: 10.1034/j.1600-0692.2003.00536.x.
    Open DOISearch in Google Scholar
  8. 8. Zivkovic, Z., Mitevska, N., Mihajlovic, I. & Nikolic, Dj. (2010). Copper losses in sulfide concentrate smelting slag are dependent on slag composition.27, 141–147.
    Search in Google Scholar
  9. 9. Acuna, C. & Sherrington, M. (2005). Slag cleaning processes: A growing concern., 475, 2745–2752. DOI:.
    Open DOISearch in Google Scholar
  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., Sect. B, 45, 23–34. DOI: 10.2298/JMMB0901023Z.
    Open DOISearch in Google Scholar
  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.
    Search in Google Scholar
  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., Sect. B, 48, 143–151. DOI: 10.2298/JMMB111115012D.
    Open DOISearch in Google Scholar
  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.35, 202–207. DOI: 10.1080/08827508.2012.738731.
    Open DOISearch in Google Scholar
  14. 14. Mitevska, N., Živković, Ž. & Marinković, J. (2000). The influence of reverb slag composition on copper losses., Sect. B, 36, 63–76.
    Search in Google Scholar
  15. 15. Sridhar, R., Toguri, J.M., Simeonov, S. (1997). Copper losses and thermodynamic considerations in copper smelting.28, 191–200. DOI: 10.1007/s11663-997-0084-5.
    Open DOISearch in Google Scholar
  16. 16. Li u, J., Gui, W., Xie, Y. & Yang, C. (2014). Dynamic modeling of copper flash smelting process at a Smelter in China.38(7–8), 2206–2213. DOI:.
    Search in Google Scholar
  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., 54(3), 350–356. DO I: 10.2320/matertrans.M2012350.
    Open DOISearch in Google Scholar
  18. 18. Živković, Ž., Mihajlović I. & Nikolić Đ. (2009). Artificial neural network method applied on the nonlinear multivariate problems.4, 143–155.
    Search in Google Scholar
  19. 19. Živković, Ž., Mihajlović, I., Djurić, I. & Štrbac, N. (2010). Statistical modeling of the industrial sodium aluminate solutions decomposition process.41, 1116–1122. DOI: 10.1007/s11663-010-9407-z.
    Open DOISearch in Google Scholar
  20. 20. Kalina, J. (2014). On robust information extraction from high-dimensional data.., 9(1), 131–144. DOI: 10.5937/sjm9-5520.
    Open DOISearch in Google Scholar
  21. 21. Azlan Hussain, M. (1999). Review of the applications of neural networks in chemical control – simulation and online implementation.13, 55–68. DOI: 1016/S0954-1810(98)00011-9.
    Open DOISearch in Google Scholar
  22. 22. Bloch, G. & Denoeux, T. (2003). Neural networks for process control and optimization: two industrial applications.42, 39–51. DOI: 10.1016/S0019-0578(07)60112-8.
    Open DOISearch in Google Scholar
  23. 23. Chehreh Chelgani, S. & Jorjani, E. (2009). Artificial neural network prediction of AlOleaching recovery in the Bayer process – Jajarm alumina plant (Iran).97, 105–110. DOI: 10.1016/j.hydromet.2009.01.008.
    Open DOISearch in Google Scholar
  24. 24. Jang, J.S.R. (1993). ANFIS: Adaptive-network based fuzzy inference system.., 23, 665–685. DOI: 10.1109/21.256541.
    Open DOISearch in Google Scholar
  25. 25. Savic, M., Mihajlovic I. & Zivkovic, Z. (2013). An ANFIS-based air quality model for prediction of SOconcentration in urban area.., 8, 25–38. DOI: 10.2139/ssrn.2257533.
    Open DOISearch in Google Scholar
  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.., 22, 459–463. DOI: 10.1016/j.ijmst.2012.06.001.
    Open DOISearch in Google Scholar
  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.37, 5705–5714. DOI: 10.1016/j.apm.2012.11.008.
    Open DOISearch in Google Scholar
  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.63, 226–235. DOI: 1016/j.ssci.2013.11.013.
    Open DOISearch in Google Scholar
  29. 29. Mihajlović, I., Đurić, I. & Živković, Ž. (2014). ANFIS based prediction of the aluminum extraction from boehmite bauxite in the Bayer process.DOI: 10.2478/pjct-2014-0018.
    Open DOISearch in Google Scholar
  30. 30. Moroney, R.N. (1998). Spurious of virtual correlation errors commonly encountered in reduction of scientific data.., 77&78, 543–553.
    Open DOISearch in Google Scholar
  31. 31. Demuth, H. & Beale, M. (2002).. The MathWorks Inc., Natick, MA.
    Search in Google Scholar
  32. 32. Jang, J.S.R., Sun, C.T. & Mizutani, E. (1997).. Prentice Hall, Cambridge, MA.
    Open DOISearch in Google Scholar
  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.43, 145–154. DOI: 10.1007/s11663-011-9570-x.
    Open DOISearch in Google Scholar
  34. 34. Takagi, T. & Sugeno, M. (1985). Fuzzy identification systems and its application to modeling and control.15, 116–132. DOI: 10.1109/tsmc.1985.6313399.
    Open DOISearch in Google Scholar
  35. 35. MATLAB, V.7.1 (2007). The MathWorks Inc., Natick, MA.
    Search in Google Scholar
DOI: https://doi.org/10.1515/pjct-2015-0051 | Journal eISSN: 3072-0389 (formerly 1899-4741) | Journal ISSN: 1509-8117
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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: Volume open
Keywords:
modeling,
copper concentrate,
smelting process,
copper losses,
slag,
ANFIS
Related subjects:
Industrial chemistry,
Biotechnology,
Chemical engineering,
Process engineering

© 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.

Volume 17 (2015): Issue 3 (September 2015)
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