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

• Authors: Ivan Mihajlović , Isidora Đurić , Živan Živković
• Languages of publication: EN

Abstracts

EN

This paper presents the results of nonlinear statistical modeling of the bauxite leaching process, as part of Bayer technology for alumina production. Based on the data, collected during the year 2011 from the industrial production in the alumina factory Birač, Zvornik (Bosnia and Herzegovina), nonlinear statistical modeling of the industrial process was performed. The model was developed as an attempt to define the dependence of the Al2O3 degree of recovery as a function of input parameters of the leaching process: content of Al2O3, SiO2 and Fe2O3 in the bauxite, as well as content of Na2Ocaustic and Al2O3 in the starting sodium aluminate solution. As the statistical modeling tool, Adaptive Network Based Fuzzy Inference System (ANFIS) was used. The model, defined by the ANFIS methodology, expressed a high fitting level and accordingly can be used for the efficient prediction of the Al2O3 degree of recovery, as a function of the process inputs under the industrial conditions.

Keywords

EN

Leaching; bauxite; Bayer process; statistical modeling; ANFIS

• Publisher: De Gruyter Open
• Journal: Polish Journal of Chemical Technology
• Year: 2014
• Volume: 16
• Issue: 1
• Pages: 103-109

Dates

published

1 - 03 - 2014

online

25 - 03 - 2014

Contributors

author

Ivan Mihajlović

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

author

Isidora Đurić

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

author

Živan Živković

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

References

• 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.[Crossref]
• 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.[Crossref]
• 3. Habashi, F. (1997). Handbook of Extractive Metallurgy. Hoboken, New Jersey, NY, USA: John Wiley & Sons, Inc.
• 4. Habashi, F. (2009). Recent trends in extractive metallurgy. J. Mining Metall. 45 B(1), 1-13. DOI: 10.2298/JMMB0901001H.[Crossref][WoS]
• 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.[Crossref]
• 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.[Crossref]
• 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.[Crossref]
• 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.
• 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.[Crossref]
• 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.[Crossref]
• 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.
• 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.[Crossref]
• 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.[Crossref]
• 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.[Crossref]
• 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.[Crossref][WoS]
• 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.[Crossref][WoS]
• 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.[Crossref]
• 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.[Crossref]
• 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.[WoS][Crossref]
• 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.[WoS][Crossref]
• 21. PASW Statistics, V.18, Formerly called SPSS Statistics, SPSS Inc.
• 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.[Crossref]
• 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.[WoS][Crossref]
• 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.[Crossref][WoS]
• 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.pdf
• 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.[Crossref]
• 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.[Crossref]
• 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.[Crossref]
• 29. MATLAB, V.7.1, The MathWorks Inc., Natick, MA, 2007.

Identifiers

DOI

10.2478/pjct-2014-0018