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Estimation of froth flotation recovery and collision probability based on operational parameters using an artificial neural network

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Abstract

An artificial neural network and regression procedures were used to predict the recovery and collision probability of quartz flotation concentrate in different operational conditions. Flotation parameters, such as dimensionless numbers (Froude, Reynolds, and Weber), particle size, air flow rate, bubble diameter, and bubble rise velocity, were used as inputs to both methods. The linear regression method shows that the relationships between flotation parameters and the recovery and collision probability of flotation can achieve correlation coefficients (R 2) of 0.54 and 0.87, respectively. A feed-forward artificial neural network with 3-3-3-2 arrangement is able to simultaneously estimate the recovery and collision probability as the outputs. In testing stages, the quite satisfactory correlation coefficient of 0.98 was achieved for both outputs. It shows that the proposed neural network models can be used to determine the most advantageous operational conditions for the expected recovery and collision probability in the froth flotation process.

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References

  1. B.V. Derjaguin and S.S. Dukhin, Theory of flotation of small and medium-size particles, Trans. Inst. Min. Metall., 70(1961), p.221.

    Google Scholar 

  2. H.J. Schulze, Physico-chemical Elementary Processes in Flotation: An Analysis from the Point of View of Colloid Science Including Process Engineering Considerations, Elsevier, 1984, p.28.

  3. J. Ralston, D. Fornasiero, and R. Hayes, Bubble-particle attachment and detachment in flotation, Int. J. Miner. Process., 56(1999), No.1–4, p.133.

    Article  CAS  Google Scholar 

  4. H. Schubert, On the turbulence-controlled microprocesses in flotation machines, Int. J. Miner. Process., 56(1999), No.1–4, p.257.

    Article  CAS  Google Scholar 

  5. B.V. Derjaguin and S.S. Dukhin, Kinetic theory of the flotation of fine particles, [in] Proceedings of the 13th International Mineral Processing Congress, Warsaw, 1979, p.21.

  6. Y. Hu, G. Qiu, and D. Wang, The studies of carrier flotation of ultrafine wolframite, Trans. Nonferrous Met. Soc. China, 2(1994), p.44.

    Google Scholar 

  7. F. Lafuma, K. Wong, and B. Cabane, Bridging of colloidal particles through adsorbed polymers, J. Colloid Interface Sci., 143(1991), No.1, p.9.

    Article  CAS  Google Scholar 

  8. I.A. Valioulis and E.J. List, Collision efficiencies of diffusing spherical particles: hydrodynamic, van der Waals and electrostatic forces, Adv. Colloid Interface Sci., 20(1984), p.1.

    Article  CAS  Google Scholar 

  9. L.J. Warren, Ultrafine particles in flotation, [in] Principles of Mineral Flotation, Victoria, 1984, p.185.

  10. R. Crawford and J. Ralston, The influence of particle size and contact angle in mineral flotation, Int. J. Miner. Process., 23(1988), p.1.

    Article  CAS  Google Scholar 

  11. P.D. Wasserman, Neural Computing: Theory and Practice, Van Nostrant Reinhold, New York, 1989.

    Google Scholar 

  12. R.T. Beale and H. Adam, Neural Computing: An Introduction, Van Nostrant Reinhold, Bristol, 1990.

    MATH  Google Scholar 

  13. C. Acharya, S. Mohanty, L.B. Sukla, and V.N. Misra, Prediction of sulphur removal with Acidithiobacillus sp. using artificial neural networks, Ecol. Modell., 190(2006), p.223.

    Article  CAS  Google Scholar 

  14. E. Jorjani, S. Chehreh Chelgani, and Sh. Mesroghli, Prediction of microbial desulfurization of coal using artificial neural networks, Miner. Eng., 20(2007), p.1285.

    Article  CAS  Google Scholar 

  15. E. Jorjani, S. Chehreh Chelgani, and Sh. Mesroghli, Application of artificial neural networks to predict chemical desulfurization of Tabas coal, Fuel, 87(2008), p.2727.

    Article  CAS  Google Scholar 

  16. S. Chehreh Chelgani and E. Jorjani, Artificial neural network prediction of Al2O3 leaching recovery in the Bayer process at the Jajarm alumina plant (Iran), Hydrometallurgy, 97(2009), p.105.

    Article  CAS  Google Scholar 

  17. B. Venkoba Raoa and S.J. Gopalakrishna, Hardgrove grindability index prediction using support vector regression, Int. J. Miner. Process., 91(2009), No.1–2, p.55.

    Article  CAS  Google Scholar 

  18. S. Chehreh Chelgani, J.C. Hower, E. Jorjani, et al., Prediction of coal grindability based on petrography, proximate and ultimate analysis using multiple regression and artificial neural network models, Fuel Process. Technol., 89(2008), p.13.

    Article  CAS  Google Scholar 

  19. E.C. Cilek, Application of neural networks to predict locked cycle flotation test results, Miner. Eng., 15(2002), p.1095.

    Article  CAS  Google Scholar 

  20. J. Labidi, M.A. Pèlach, X. Turon, and P. Mutjé, Predicting flotation efficiency using neural networks, Chem. Eng. Process., 46(2007), p.314.

    Article  CAS  Google Scholar 

  21. P. Clifford, M. Lloyd, and P. Zhang, Technology research improves phosphate economics, Miner. Eng., 98(1998), p.46.

    Google Scholar 

  22. W.J. Rodrigues, L.S.L. Filho, and E.A. Masini, Hydrodynamic dimensionless parameters and their Influence on flotation performance of coarse particles, Miner. Eng., 14(2001), p.1047

    Article  CAS  Google Scholar 

  23. R. Roscoe, The viscosity of suspensions of rigid spheres, Br. J. Appl. Phys., 3(1952), p.267.

    Article  ADS  Google Scholar 

  24. E.H. Girgin, S. Do, C.O. Gomez, and J.A. Finch, Bubble size as a function of impeller speed in a self-aeration laboratory flotation cell, Miner. Eng., 19(2006), p.201.

    Article  CAS  Google Scholar 

  25. R.T. Rodrigues and J. Rubio, New basis for measuring the size distribution of bubbles, Miner. Eng., 16(2003), p.757.

    Article  CAS  Google Scholar 

  26. R.H. Yoon, The role of hydrodynamic and surface forces in bubble-particle interaction, Int. J. Miner. Process., 58(2000), No.1–4, p.129.

    Article  MathSciNet  CAS  Google Scholar 

  27. M.A. Hakeem, M. Kamil, and I. Arman, Prediction of temperature profiles using artificial neural networks in a vertical thermosiphon reboiler, Appl. Therm. Eng., 28(2008), p.1572.

    Article  CAS  Google Scholar 

  28. A. Afkhami, M. Abbasi-Tarighat, and M. Bahramb, Artificial neural networks for determination of enantiomeric composition of α-phenylglycine using UV spectra of cyclodextrin host-guest complexes. Comparison of feed-forward and radial basis function networks, Talanta, 75(2008), p.91.

    Article  PubMed  CAS  Google Scholar 

  29. F.F. Farshad, J.D. Garber, and J.N. Lorde, Predicting temperature profiles in producing oil wells using artificial neural networks, Eng. Comput., 17(2000), p.735.

    Article  MATH  Google Scholar 

  30. E. Razmi-Rad, B. Ghanbarzadeh, S.M. Mousavi, et al., Prediction of rheological properties of Iranian bread dough from chemical composition of wheat flour by using artificial neural networks, J. Food Eng., 81(2007), p.728.

    Article  Google Scholar 

  31. A. Alonso-Rodriguez, Forecasting economic magnitudes with neural network models, Int. Adv. Econ. Res., 5(1999), No.4, p.496.

    Article  Google Scholar 

  32. S. Landau and S.E. Brian, A Handbook of Statistical Analyses Using SPSS, Chapman Hall/Crc, New York, 2004, p.113.

    Google Scholar 

  33. K. Smith, Neural Networks in Business: Techniques and Applications, Idea Group Publishing, Hershey, 2002.

    Google Scholar 

  34. H. Demuth and M. Beale, Neural Network Toolbox for Use with MATLAB, The Mathworks Inc., Natick, 2002, p.154.

    Google Scholar 

  35. C. Aldrich, Exploratory Analysis of Metallurgical Process Data with Neural Networks and Related Methods, Elsevier, Amsterdam, 2002, p.5.

    Google Scholar 

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Authors and Affiliations

  1. Surface Science Western, Research Park, University of Western Ontario, London, Ont., N6G 0J3, Canada

    Saeed Chehreh Chelgani

  2. Mining Engineering Department, Science and Research Branch, Islamic Azad University, Tehran, 1477893855, Iran

    Behzad Shahbazi

  3. Amirkabir University of Technology, Tehran, 158754413, Iran

    Bahram Rezai

Authors
  1. Saeed Chehreh Chelgani
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  2. Behzad Shahbazi
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  3. Bahram Rezai
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Correspondence to Saeed Chehreh Chelgani.

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Chelgani, S.C., Shahbazi, B. & Rezai, B. Estimation of froth flotation recovery and collision probability based on operational parameters using an artificial neural network. Int J Miner Metall Mater 17, 526–534 (2010). https://doi.org/10.1007/s12613-010-0353-1

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  • DOI: https://doi.org/10.1007/s12613-010-0353-1

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