Comparative Study of Artificial Neural Networks (ANN) and Statistical Methods for Predicting the Performance of Ultrafiltration Process in the Milk Industry

Document Type : Research Article


1 Department of Chemical Engineering, University of Sistan and Baluchestan, Zahedan, I.R. IRAN

2 Department of Chemical Engineering, University of Ferdowsi, Mashad, I.R. IRAN


Milk ultrafiltration is a membrane process, which is highly complex innature. The cost effectiveness of the process depends heavily on the flux permeate and the total hydraulic resistance of the membrane. In this work, a comparative study for the prediction of the performance of milk ultrafiltration with ANN and statistical method has been carried out. The result reveals that both methods carry out the prediction with a high degree of accuracy. However, the statistical method, contrary to neural nets, is both costly and time consuming and the accuracy of the data are also in doubt, as the operating conditions are not consistent throughout each of the test runs. The result also reveals that there is a good agreement between the predicted fluxes permeates and the total resistances of this work with the actual values. The findings of this study also shows that the artificial neural nets technique can be applied as a powerful tool and a cost and time effective way in predicting and assessing the performance of  milk ultrafiltration process. 


Main Subjects

[1] Rautenbach, R., Albrecht, R., “Membrane Processes”, Translated by Valeri Cottrell, John Wiley & Sons Ltd., New York, (1989).   
[2] Clarke, T.E. and Heath, C.A., Ultrafiltration of Skim Milk in Flat-Plate and Spiral-Wound Modules, Journal of Food Engineering, 33, p. 373 (1997).
[3] Grandison, A.S., Youravong, W. & Lewis, M.J., Hydrodynamics Factors Affecting Flux and Fouling During Ultrafiltration of Skimmed Milk, Lait, 80,
p. 165 (2000).
[4] Cheryan, M., “Ultrafiltration and Microfiltration Handbook”, Techomic Publishing Ltd., Lancaster, (1998).
[5]  Glover, F. A., Skudder, P. J. and Stothart, P. H., Reviews of the Progress of Dairy Science, Reverse Osmosis and Ultrafiltration in Dairying, Journal of Dairy Research, 45, p. 291 (1978).
[6] Krstic, D.M., Tekic, M.N., Caric, M.D. and Milanoric, S.D., The Effect of Turbulence Promoter on Cross-Flow Microfiltration of Skim Milk, Journal of Membrane Science, 208, p. 303 (2002).
[7] Gardson, G.D., “Neural Networks”, SAGE Publi-cations Ltd., London, (1998).
[8] Niemi, H., Bulsari, A. and Palosaari, S., Simulation of Membrane Separation by Neural Networks, Journal of Membrane Science, 102, p. 185 (1995).
[9] Bowen W.R., Jones M.J., Yousef H.N.S., Prediction of the Rate of Crossflow Membrane Ultrafiltration of Colloids: A Neural Network Approach, Chemical Engineering Science, 53, p. 3793 (1998).
[10] Neuro Dimensions, NeuroSolutions, 2001. www. nd. com.
[11] Zupan,  J.,  Gasteiger, J., “Neural Networks in Chemistry and Drug Design”, Wiley-VCH, Weinheim, (1999).
[12] Demuth, H., Beale, M., “Neural Network Toolbox User’s Guide”, The Mathworks, Natick, (2000).
[13] Kasabov, N.K., Foundations of Neural Networks, Fuzzy Systems and Knowledge Engineering, MIT Press, Cambridge, (1998).
[14] Hagan, T. M., Demuth, H. B., Beale, M., “Neural Network Design”, PWS Publishing Company, USA, (1996).
[15] Norgaard, M., Ravn, O., Poulsen, N.K., Hansen,   L.K., “Neural Networks for Modeling and Control of Dynamic Systems”, Springer, Heidelberg, (1999).
[16] Hornik, K., Stinchcombe, M., White, H., Multilayer Feed Forward Networks are Universal Approximators, Neural Network, 2, p. 359 (1989).
[17] Menhaj M. B., “Fundamentals of Neural Networks”, Professor Hesabi publications, Tehran, (1998).
[18] Fane, A. G., “Ultrafiltration,  Factors  Influencing Flux”, Progress in Filtration and Separation, 101-179, (1986).
[19] Fenton, M.R.I., Concentration and Fractionation of Skim Milk by Reverse Osmosis and Ultrafiltration, Journal of Dairy Science, 55 (11), p. 1561 (1972).
[20] Pompei,  C., Skim Milk  Protein  Recovery and Purification by Ultrafiltration, Influence of Tem-perature on Permeation Rate and Retention, Journal of Food Science, 38 (5), p. 867 (1973).
[21] Dornier, M., Decloux, M., Trystram, G. and Lebert, A., Dynamic Modeling of Cross Flow Microfiltration Using Neural Networks, Journal of Membrane Science, 98, p. 263 (1995).
[22] Teodosiu,  C.,  Pastravanu,  O. and Macoveanu, M., Neural Network Models for Ultrafiltration and Backwashing, Water Research, 34, p. 4371 (2000).