ANFIS based prediction of the aluminum extraction from boehmite bauxite in the Bayer process

1. Đurić, I., Mihajlović, I. & Živković, Ž. (2010). Kinetic Modelling of Different Bauxite Types in the Bayer Leaching Process. Can. Metall. Q. 49(3), 209-218. DOI: 10.1179/00084 4310795937730.Search in Google Scholar

2. Gontijo, G.S., Brandao de Araujo, A.C., Prasad, S., Vasconcelos, L.G.S., Alves, J.J.N. & Brito, R.P. (2009). Improving the Bayer process productivity - An industrial case study. Min. Eng. 22, 1130-1136. DOI: 10.1016/j.mineng.2009.04.010.10.1016/j.mineng.2009.04.010Search in Google Scholar

3. Habashi, F. (1997). Handbook of Extractive Metallurgy. Hoboken, New Jersey, NY, USA: John Wiley & Sons, Inc.Search in Google Scholar

4. Habashi, F. (2009). Recent trends in extractive metallurgy. J. Mining Metall. 45 B(1), 1-13. DOI: 10.2298/JMMB0901001H.10.2298/JMMB0901001HSearch in Google Scholar

5. Zhang, Y.F., Li, Y.H. & Zhang, Y. (2003). Phase diagram for the system Na2O-Al2O3-H2O at high alkali concentration. J. of Chem. & Eng. Data. 48(3), 617-620. DOI: 10.1021/je025611g.10.1021/je025611gSearch in Google Scholar

6. Whittington, B.I., Fletcher, B.L. & Talbot, C. (1998). The effect of reaction conditions on the composition of desilication product (DSP) formed under simulated Bayer conditions. Hydrometall. 49, 1-22. DOI: 10.1016/S0304-386X(98)00021-8.10.1016/S0304-386X(98)00021-8Search in Google Scholar

7. Jamialahmadi, M. & Muller-Steinhagen, H. (1998). Determining silica solubility in Bayer process liquor. JOM. 50(11), 44-49. DOI: 10.1007/s11837-998-0286-6.10.1007/s11837-998-0286-6Search in Google Scholar

8. Palmer, D.A., Benezeth, P., Weselowski, D.J. & Helc, S. (2003). Experimental study of the dissolution of aluminum phases as a function of temperature, caustic concentration and additives. In Light Metals Symposium. 15-17 November 2001 (pp. 5-10), Warrendale, Pennsylvania. USA, The Minerals, Metals & Materials Society.Search in Google Scholar

9. Xu, B., Wingate, C. & Smith, P. (2009). The effect of surface area on the modelling of quartz dissolution under conditions relevant to the Bayer process. Hydrometall. 98, 108-115. DOI:10.1016/j.hydromet.2009.04.006.10.1016/j.hydromet.2009.04.006Search in Google Scholar

10. Chelgani, S.C. & Jorjani, E. (2009). Artifi cial neural network prediction of Al2O3 leaching recovery in the Bayer process - Jajarm alumina plant (Iran). Hydrometall. 97, 105-110. DOI:10.1016/j.hydromet.2009.01.008.10.1016/j.hydromet.2009.01.008Search in Google Scholar

11. Songqing, G., Zhonling, Y. & Lijuan, Q. (2002). Investing method of Bayer digestion process of diasporic bauxite in China. In Light Metals Symposium. 17-21 February 2002 (pp. 83-88), Warrendale, Pennsylvania. USA, The Minerals, Metals & Materials Society.Search in Google Scholar

12. Pereira, J.A.M., Schwaab, M., Dell’Oro, E., Pinto, J.C., Monteiro, J.L.F. & Henriques, C.A. (2009). The kinetics of gibbsite dissolution in NaOH. Hydrometall. 96, 6-13. DOI: 10.1016/j.hydromet.2008.07.009.10.1016/j.hydromet.2008.07.009Search in Google Scholar

13. Panias, D., Asimidis, P. & Paspaliaris, I. (2001). Solubility of boehmite in concentrated sodium hydroxide solutions. Model development and assessment. Hydrometall. 59, 15-29. DOI: 10.1016/S0304-386X(00)00146-8.10.1016/S0304-386X(00)00146-8Search in Google Scholar

14. Cao, S., Zhang, Y.F. & Zhang, Y. (2009). Preparation of sodium aluminate from the leach liquor of diaspoiric bauxite in concentration NaOH solution. Hydrometall. 98, 298-303. DOI:10.1016/j.hydromet.2009.05.016.10.1016/j.hydromet.2009.05.016Search in Google Scholar

15. Đurić, I., Đorđević, P., Mihajlović, I., Nikolić, Đ. & Živković, Ž. (2010). Prediction of Al2O3 leaching recovery in the Bayer process using statistical multilinear regression analysis. J. Mining Metall. 46(2) B 161-169. DOI:10.2298/JMMB1002161D.10.2298/JMMB1002161DSearch in Google Scholar

16. Đurić, I., Mihajlović, I., Živković & Kešelj, D. (2011). Artiffcial neural network prediction of aluminum extraction from bauxite in the Bayer process. J. Serb. Chem. Soc. 76(9) 1259-1271. DOI: 10.2298/JSC110526193D.10.2298/JSC110526193DSearch in Google Scholar

17. Wernick, P. & Lehman, M.M. (1999). Software process white box modelling for FEAST/1. J. Syst. Software.46, 193-201. DOI: 10.1016/S0164-1212(99)00012-6.10.1016/S0164-1212(99)00012-6Search in Google Scholar

18. Davoody, M., Zahedi, G., Biglari, M., Meireles, M.A.A. & Bahadori, A. (2012). Expert and gray box modeling of high pressure liquid carbon dioxide extraction of Pimpinella anisum L. seed. J. Supercritical Fluids. 72, 213-222. DOI: 10.1016/j. supfl u.2012.09.002.Search in Google Scholar

19. Mjalli, F.S., Al-Asheh, S. & Alfadala, H.E. (2007). Use of artificial neural network black-box modeling for the prediction of wastewater treatment plants performance. J. Environ. Manage. 83, 329-338. DOI: 10.1016/j.jenvman.2006.03.004.10.1016/j.jenvman.2006.03.00416806660Search in Google Scholar

20. Prada, L., Garcia, J., Calderon, A., Garcia, J.D. & Carretero, J. (2013). A novel black-box simulation model methodology for predicting performance and energy consumption in commodity storage devices. Simul. Model. Pract. Th. 34, 48-63. DOI: 10.1016/j.simpat.2013.01.006.10.1016/j.simpat.2013.01.006Search in Google Scholar

21. PASW Statistics, V.18, Formerly called SPSS Statistics, SPSS Inc. Search in Google Scholar

22. Mihajlović, I. Štrbac, N., Đorđević, P., Ivanović, A. & Živković, Ž. (2011). Technological process modeling aiming to improve its operations management. Serb. J. Manag. 6 (2), 135-144. DOI: 10.5937/sjm1102135M.10.5937/sjm1102135MSearch in Google Scholar

23. Yetilmezsoy, K., Fingas, M. & Fieldhouse, B. (2011). An adaptive neuro-fuzzy approach for modeling of water-in- -oil emulsion formation. Colloid. Surface. A. 389(1-3), 50-62. DOI: 10.1016/j.colsurfa.2011.08.051.10.1016/j.colsurfa.2011.08.051Search in Google Scholar

24. Canete, J.F., Garcia-Cerezo, A., Garcia-Moral, I., Del Saz, P. & Ochoa, E. (2013). Object-oriented approach applied to ANFIS modeling and control of a destillation column. Expert Syst. Appl. 40(14), 5648-5660. DOI: 10.1016/j.eswa.2013.04.012.10.1016/j.eswa.2013.04.012Search in Google Scholar

25. Chauhan, S., Singh, M. & Meena, V.K. (2013). Comparative study of BOF steelmaking process based on ANFIS and GRNN model. IJEIT. 2(9), 198-202. Retrieved November 15, 2013, from http://ijeit.com/vol%202/Issue%209/IJEIT141220130336.pdfSearch in Google Scholar

26. Takagi, T. & Sugeno, M. (1985). Fuzzy identifi cation of systems and its application to modeling and control. IEEE Trans. Systems. Man. Cybernetics. 15(1), 116-132. DOI: 0018-9472/85/0100-0116$01.00.10.1109/TSMC.1985.6313399Search in Google Scholar

27. Jang, M., Cai, L., Udeani, G., Slowing, K., Thomas, K., Beecher, C., Fong, H., Farnsworth, N., Kinghorn, A.D., Mehta, R., Moon, R. & Pezzuto, J. (1997). Cancer Chemopreventive Activity of Resveratrol, a Natural Product Derived from Grapes. Sci. Magazine. 275, 218-220. DOI: 10.1126/science.275.5297.218.10.1126/science.275.5297.2188985016Search in Google Scholar

28. Savić, M., Mihajlović, I. & Živković, Ž. (2013). An ANFIS - Based Air Quality Model for Prediction of SO2 Concentration in Urban Area. Serb. J. Manag. 8(1), 25-38. DOI: 10.5937/sjm8-3295.10.5937/sjm8-3295Search in Google Scholar

29. MATLAB, V.7.1, The MathWorks Inc., Natick, MA, 2007. Search in Google Scholar