A Fuzzy Logic Model to Predict the Bioleaching Efficiency of Copper Concentrates in Stirred Tank Reactors

Ali Ahmadi, Mohammad Raouf Hosseini

Department of Mining Engineering, Isfahan University of Technology, Isfahan, Iran.

DOI: 10.4236/ijnm.2015.41001                

Abstract

Multiplicity of the chemical, biological, electrochemical and operational variables and nonlinear behavior of metal extraction in bioleaching environments complicate the mathematical modeling of these systems. This research was done to predict copper and iron recovery from a copper flotation concentrate in a stirred tank bioreactor using a fuzzy logic model. Experiments were carried out in the presence of a mixed culture of mesophilic bacteria at 35° C, and a mixed culture of moderately thermophilic bacteria at 50° C. Input variables were method of operation (bioleaching or electrobioleaching), the type of bacteria and time (day), while the recoveries of copper and iron were the outputs. A relationship was developed between stated inputs and the outputs by means of “if-then” rules. The resulting fuzzy model showed a satisfactory prediction of the copper and iron extraction and had a good correlation of experimental data with R-squared more than 0.97. The results of this study suggested that fuzzy logic provided a powerful and reliable tool for predicting the nonlinear and time variant bioleaching processes.

Keywords

Fuzzy Logic, Modeling, Copper Concentrate, Bioleaching, Stirred Reactor

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Rossi, G. (1990) Biohydrometallurgy. McGraw-Hill, Boston.
[2]	Pham, D. and Pham, P. (1999) Artificial Intelligence in Engineering. International Journal of Machine Tools and Manufacture, 39, 937-949. http://dx.doi.org/10.1016/S0890-6955(98)00076-5
[3]	Zadeh, L.A. (1965) Fuzzy Sets. Information and Control, 8, 338-353. http://dx.doi.org/10.1016/S0019-9958(65)90241-X
[4]	Hsiang, S. and Lin, Y. (2008) Application of Fuzzy Theory to Predict Deformation Behaviors of Magnesium Alloy Sheets under Hot Extrusion. Journal of Materials Processing Technology, 201, 138-144. http://dx.doi.org/10.1016/j.jmatprotec.2007.11.222
[5]	Bergh, L., Yianatos, J. and Leiva, C. (1998) Fuzzy Supervisory Control of Flotation Columns. Minerals Engineering, 11, 739-748. http://dx.doi.org/10.1016/S0892-6875(98)00059-4
[6]	http://www.outotec.com/en/Products--services/Analyzers-and-automation/Zinc-refining-control-solutions/
[7]	Abou, S.C. and Dao, T.-M. (2009) Fuzzy Logic Controller Based on Association Rules Mining: Application to Mineral Processing. Proceedings of the World Congress on Engineering and Computer Science, 2.
[8]	Carvalho, M.T. and Durão, F. (2002) Control of a Flotation Column Using Fuzzy Logic Inference. Fuzzy Sets and Systems, 125, 121-133. http://dx.doi.org/10.1016/S0165-0114(01)00048-3
[9]	Vieira, S., Sousa, J. and Durao, F. (2005) Fuzzy Modelling Strategies Applied to a Column Flotation Process. Minerals Engineering, 18, 725-729. http://dx.doi.org/10.1016/j.mineng.2004.10.008
[10]	Saravani, A., Mehrshad, N. and Massinaei, M. (2014) Fuzzy-Based Modeling and Control of an Industrial Flotation Column. Chemical Engineering Communications, 201, 896-908. http://dx.doi.org/10.1080/00986445.2013.790815
[11]	Petersen, J. and Dixon, D. (2007) Modelling Zinc Heap Bioleaching. Hydrometallurgy, 85, 127-143. http://dx.doi.org/10.1016/j.hydromet.2006.09.001
[12]	Bennett, C., McBride, D., Cross, M. and Gebhardt, J. (2012) A Comprehensive Model for Copper Sulphide Heap Leaching: Part 1 Basic Formulation and Validation Through Column Test Simulation. Hydrometallurgy, 127, 150-161. http://dx.doi.org/10.1016/j.hydromet.2012.08.004
[13]	Ahmadi, A., Ranjbar, M., Schaffie, M. and Petersen, J. (2012) Kinetic Modeling of Bioleaching of Copper Sulfide Concentrates in Conventional and Electrochemically Controlled Systems. Hydrometallurgy, 127, 16-23. http://dx.doi.org/10.1016/j.hydromet.2012.06.010
[14]	Leahy, M.J., Davidson, M.R. and Schwarz, M.P. (2005) A Two-Dimensional CFD Model for Heap Bioleaching of Chalcocite. ANZIAM Journal, 46, C439-C457.
[15]	Gonzalez, R., Gentina, J.C. and Acevedo, F. (2004) Biooxidation of a Gold Concentrate in a Continuous Stirred Tank Reactor: Mathematical Model and Optimal Configuration. Biochemical Engineering Journal, 19, 33-42. http://dx.doi.org/10.1016/j.bej.2003.09.007
[16]	Pazouki, M., Ganjkhanlou, Y., Tofigh, A., Hosseini, M., Aghaie, E. and Ranjbar, M. (2012) Optimizing of Iron Bioleaching from a Contaminated Kaolin Clay by the Use of Artificial Neural Network. International Journal of Engineering-Transactions B: Applications, 25, 81-88.
[17]	Ahmadi, A., Schaffie, M., Manafi, Z. and Ranjbar, M. (2010) Electrochemical Bioleaching of High Grade Chalcopyrite Flotation Concentrates in a Stirred Bioreactor. Hydrometallurgy, 104, 99-105. http://dx.doi.org/10.1016/j.hydromet.2010.05.001
[18]	Mendel, J.M. (1995) Fuzzy Logic Systems for Engineering: A Tutorial. Proceedings of the IEEE, 83, 345-377. http://dx.doi.org/10.1109/5.364485
[19]	Kasabov, N.K. (1996) Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering. Marcel Alencar, New York.
[20]	Nguyen, H.T., Prasad, N.R., Walker, C.L. and Walker, E.A. (2010) A First Course in Fuzzy and Neural Control. CRC Press, Boca Raton.
[21]	Nguyen, H.T. and Walker, E.A. (2005) A First Course in Fuzzy Logic. CRC Press, Boca Raton.