225
Views
5
CrossRef citations to date
0
Altmetric
Original Articles

Prediction of Gas Holdup in a Flotation Column by Artificial Neural Network

, &
Pages 165-175 | Received 28 Feb 2014, Accepted 16 Apr 2014, Published online: 15 Apr 2015

References

  • Yianatos, J. B., J. A. Finch, G. S. Dobby, and M. Xu. 1988. Bubble size estimation in a bubble swarm. Journal of Colloid and Interface Science 126 (1): 37–44.
  • Cilek, E. C., and B. Z. Yilmazer. 2003. Effects of hydrodynamic parameters on entrainment and flotation performance. Minerals Engineering 16: 745–756.
  • Thyabats, A. 2008. On the optimization of froth flotation by the use of an artificial neural network. Journal of China University of Mining and Technology 18: 418–426.
  • Jorjani, E., H. Asadollahi Poorali, A. Sam, S. C. Chelgani, S. Mesroghli, and M. R. Shayestehfar. 2009. Prediction of coal response of froth flotation based on coal analysis using regression and artificial neural network. Minerals Engineering 22: 970–976.
  • Massinaei, M., and R. Doostmohammadi. 2010. Modeling of bubble surface area flux in an industrial rougher column using artificial neural network and statistical techniques. Minerals Engineering 23: 83–90.
  • Nakhaei, F., M. R. Mosavi, A. Sam, Y. Vaghei. 2012. Recovery and grade accurate prediction of pilot plant flotation column concentrate: Neural network and statistical techniques. International Journal of Mineral Processing 110: 140–154.
  • Nakhaei, F., M. R. Mosavi, and A. Sam. 2013. Recovery and grade prediction of pilot plant flotation column concentrate by a hybrid neural genetic algorithm. International Journal of Mining Science and Technology 23: 69–77.
  • Nakhaei, F., and M. Irannajad. 2013. Comparison between neural networks and multiple regression methods in metallurgical performance modeling of flotation column. Physicochemical Problems of Mineral Processing 49: 255–266.
  • Nakhaei, F., and A. Sam. 2010. Prediction of copper grade at flotation column concentrate using artificial neural network. Paper presented at the IEEE Conference on Signal Processing, Beijing, China.
  • Xie, T., S. M. Ghiaasiaan, and S. Karrila. 2004. Artificial neural network approach for flow regime classification in gas-liquid-fiber flows based on frequency domain analysis of pressure signals. Chemical Engineering Science 59: 2241–2251.
  • Gupta, S., P. Liu, S. Svoronos, R. Sharma, N. A. Abdel-Khalek, and H. El-Shall. 1999. Hybrid first-principles/neural networks model for column flotation. AICHE Journal 45 (3): 557–566.
  • PAControl.com
  • Si-Moussa, C., S. Hanini, R. Derriche, M. Bouhedda, and A. Bouzidi. 2008. Prediction of high-pressure vapor liquid equilibrium of six binary systems, carbon dioxide with six esters, using an artificial neural network model. Brazilian Journal of Chemical Engineering 25: 183–199.
  • Razavi, M. A., A. Mortazavi, and M. Mousavi. 2003. Dynamic modeling of milk ultrafiltration by artificial neural network. Journal of Membrane Science 220: 47–58.
  • Musavi, M. T., M. Qiao, M. T. Davisson, and E. C. Akeson. 1996. Classification of mouse chromosomesusing artificial neural networks. In IEEE International Conference on Neural Networks, 852–857, Washington, DC, USA.
  • The MathWorks. 2008. MATLAB. 7.6.0. Available at: http://www.pacontrol.com/
  • Stich, T. J., J. K. Spoerre, and T. Velasco. 2000. The application of artificial neural networks to monitoring and control of an induction hardening process. Journal of Industrial Technology 16 (1): 1–11.
  • Patel, S. U., B. J. Kumar, Y. P. Badhe, B. K. Sharma, S. Biswas, and A. Chaudury. 2007. Estimation of gross calorific value of coals using artificial neural networks. Fuel 86: 334–344.
  • Bahar, A., and C. Ozgen. 2010. State estimation and inferential control for a reactive batch distillation column. Engineering Applications of Artificial Intelligence 23: 262–270.
  • Swanson, D. A., J. Tayman, and T. M. Bryan. 2011. MAPE-R: A rescaled measure of accuracy for cross-sectional subnational population forecasts. International journal of population research 28: 225–243.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.