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

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.

[1]  Michael J. Lewis,et al.  Hydrodynamic factors affecting flux and fouling during ultrafiltration of skimmed milk , 2000 .

[2]  M. Caric,et al.  The effect of turbulence promoter on cross-flow microfiltration of skim milk , 2002 .

[3]  Johann Gasteiger,et al.  Neural networks in chemistry and drug design , 1999 .

[4]  C. Heath,et al.  Ultrafiltration of skim milk in flat-plate and spiral-wound modules , 1997 .

[5]  Martin T. Hagan,et al.  Neural network design , 1995 .

[6]  Munir Cheryan,et al.  Ultrafiltration and Microfiltration Handbook , 1998 .

[7]  Tarun Khanna,et al.  Foundations of neural networks , 1990 .

[8]  D. Signorini,et al.  Neural networks , 1995, The Lancet.

[9]  P. Resmini,et al.  Skim milk protein recovery and purification by ultrafiltration. Influence of temperature on permeation rate and retention , 1973 .

[10]  Octavian Pastravanu,et al.  Neural network models for ultrafiltration and backwashing , 2000 .

[11]  C. H. Amundson,et al.  Concentration and Fractionation of Skimmilk by Reverse Osmosis and Ultrafiltration , 1972 .

[12]  Nikola Kasabov,et al.  Foundations Of Neural Networks, Fuzzy Systems, And Knowledge Engineering [Books in Brief] , 1996, IEEE Transactions on Neural Networks.

[13]  G. Trystram,et al.  Dynamic modeling of crossflow microfiltration using neural networks , 1995 .

[14]  Sandhya Samarasinghe.  Fundamentals of Neural Networks and Models for Linear Data Analysis , 2006 .

[15]  Abhay B. Bulsari,et al.  SIMULATION OF MEMBRANE SEPARATION BY NEURAL NETWORKS , 1995 .

[16]  F. A. Glover,et al.  Reverse osmosis and ultrafiltration in dairying , 1978, Journal of Dairy Research.

[17]  Kurt Hornik,et al.  FEED FORWARD NETWORKS ARE UNIVERSAL APPROXIMATORS , 1989 .

[18]  A. Fane.  Ultrafiltration: factors influencing flux and rejection , 1986 .

[19]  Derong Liu,et al.  Neural networks for modeling and control of dynamic systems: a practitioner's handbook: M. Nørgaard, O. Ravn, N.K. Poulsen, and L.K. Hansen; Springer, London, 2000, 246pp., paperback, ISBN 1-85233-227-1 , 2002, Autom..

[20]  Laurene V. Fausett,et al.  Fundamentals Of Neural Networks , 1993 .

[21]  W. Richard Bowen,et al.  Prediction of the rate of crossflow membrane ultrafiltration of colloids: A neural network approach , 1998 .