Predicting the flux decline in milk cross-flow ceramic ultrafiltration by artificial neural networks

Desalination

Volumn 250, Issue 3, 2010, Pages 1118-1120

  Guadix, Antonio ^a   Zapata, Jose E ^b   Almecija, M Carmen ^a   Guadix, Emilia M ^a  

^a UNIVERSITY OF GRANADA   (Spain)

^b UNIVERSIDAD DE ANTIOQUIA   (Colombia)

Author keywords

Artificial neural networks; Ceramic membranes; Milk; Ultrafiltration

Indexed keywords

ARTIFICIAL NEURAL NETWORK; ARTIFICIAL NEURAL NETWORK MODELS; ARTIFICIAL NEURAL NETWORKS; CLEANING PROTOCOLS; CROSS FLOWS; EXPERIMENTAL DATA; FILTRATION TIME; FLUX DECLINE; LEVENBERG-MARQUARDT; MEMBRANE PROCESS; OPERATIONAL CYCLE; PERMEATE FLUX; TRAINING ALGORITHMS; TUBULAR CERAMIC MEMBRANES;

BACKPROPAGATION; NEURAL NETWORKS; ULTRAFILTRATION; WASTEWATER TREATMENT;

CERAMIC MEMBRANES;

ALGORITHM; ARTIFICIAL NEURAL NETWORK; EXPERIMENTAL STUDY; FOULING; MEMBRANE; MILK; ULTRAFILTRATION;

EID: 71549144442     PISSN: 00119164     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.desal.2009.09.121     Document Type: Article

References (20)

1. Wessling M., Mulder M.H.V., Bos A., Van Der Linden M., Bos M., and Van Der Linden W.E. Modelling the permeability of polymers: a neural network approach. J. Membr. Sci. 86 (1994) 193-198
2. Dornier M., Decloux M., Trystram G., and Lebert A. Dynamic modeling of crossflow microfiltration using neural networks. J. Membr. Sci. 98 (1995) 263-273
3. Niemi H., Bulsari A., and Palosaari S. Simulation of membrane separation by neural networks. J. Membr. Sci. 102 (1995) 185-191
4. Delgrange N., Cabassud C., Cabassud M., Durand-Bourlier L., and Lainé J.M. Neural networks for prediction of ultrafiltration transmembrane pressure - application to drinking water production. J. Membr. Sci. 150 (1998) 111-123
5. Bowen W.R., Jones M.G., and Yousef H.N.S. Dynamic ultrafiltration of proteins - a neural network approach. J. Membr. Sci. 146 (1998) 225-235
6. Shetty G.R., Malki H., and Chellam S. Predicting contaminant removal during municipal drinking water nanofiltration using artificial neural networks. J. Membr. Sci. 212 (2003) 99-112
7. Chellam S. Artificial neural network model for transient crossflow microfiltration of polydispersed suspensions. J. Membr. Sci. 258 (2005) 35-42
8. Zhao Y., Taylor J.S., and Chellam S. Predicting RO/NF water quality by modified solution diffusion model and artificial neural networks. J. Membr. Sci. 263 (2005) 38-46
9. Fu R.Q., Xu T.W., and Pan Z.X. Modelling of the adsorption of bovine serum albumin on porous polyethylene membrane by back-propagation artificial neural network. J. Membr. Sci. 251 (2005) 137-144
10. Curcio S., Scilingo G., Calabro V., and Iorio G. Ultrafiltration of BSA in pulsating conditions: an artificial neural networks approach. J. Membr. Sci. 246 (2005) 235-247
11. Shahsavand A., and Chenar M.P. Neural networks modeling of hollow fiber membrane processes. J. Membr. Sci. 297 (2007) 59-73
12. Liu Q.F., and Kim S.H. Evaluation of membrane fouling models based on bench-scale experiments: a comparison between constant flowrate blocking laws and artificial neural network (ANNs) model. J. Membr. Sci. 310 (2008) 393-401
13. Cheng L.H., Cheng Y.F., and Chen J. Predicting effect of interparticle interactions on permeate flux decline in CMF of colloidal suspensions: an overlapped type of local neural network. J. Membr. Sci. 308 (2008) 54-65
14. Curcio S., Calabro V., and Iorio G. Reduction and control of flux decline in cross-flow membrane processes modeled by artificial neural networks. J. Membr. Sci. 286 (2006) 125-132
15. Sahoo G.B., and Ray C. Predicting flux decline in crossflow membranes using artificial neural networks and genetic algorithms. J. Membr. Sci. 283 (2006) 147-157
16. Ni Mhurchu J., and Foley G. Dead-end filtration of yeast suspensions: correlating specific resistance and flux data using artificial neural networks. J. Membr. Sci. 281 (2006) 325-333
17. Aydiner C., Demir I., and Yildiz E. Modeling of flux decline in crossflow microfiltration using neural networks: the case of phosphate removal. J. Membr. Sci. 248 (2005) 53-62
18. Razavi M.A., Mortazavi A., and Mousavi M. Dynamic modelling of milk ultrafiltration by artificial neural network. J. Membr. Sci. 220 (2003) 47-58
19. Shetty G.R., and Chellam S. Predicting membrane fouling during municipal drinking water nanofiltration using artificial neural networks. J. Membr. Sci. 217 (2003)
20. Demuth H., and Beale M. Neural Network Toolbox for use with MATLAB, The MathWorks (2002)