Prediction of membrane fouling rate by neural network modeling

Desalination and Water Treatment

Volumn 15, Issue 1-3, 2010, Pages 134-140

  Hwang, Tae Mun ^a,b   Choi, Yongjun ^c   Nam, Sook Hyun ^a   Lee, Sangho ^a   Oh, Hyunje ^a,b   Hyun, Kyounghak ^b   Choung, Youn Kyoo ^b  

^a Korea Institute of Civil Engineering and Building Technology   (South Korea)

^b Yonsei University   (South Korea)

^c University of Science and Technology (UST)   (South Korea)

Author keywords

Artificial neural network; Cake filtration; Fouling rate; Microfiltration

Indexed keywords

EID: 77954238529     PISSN: 19443994     EISSN: 19443986     Source Type: Journal    
DOI: 10.5004/dwt.2010.1677     Document Type: Article

References (9)

1. L.J. Zeman and A.L. Zydney, Microfiltration and Ultrafiltration, Marcel Dekker, New York, 1993.
2. J.N. Mhurchuú and G. Foley, Dead-end filtration of yeast suspensions: Correlating specific resistance and flux data using artificial neural networks, J. Membr. Sci., 281 (2006) 325-333.
3. K.J. Hwang and Y.J. Wu, Flux enhancement and cake formation in air-sparged cross-flow microfiltration, Chem. Eng. J., 139 (2008) 296-303.
4. G.R. Shetty and S. Chellam, Predicting membrane fouling during municipal drinking water nanofiltration using artificial neural networks, J. Membr. Sci., 217 (2003) 69-86.
5. G.B. Sahoo and C. Ray, Predicting flux decline in crossflow membranes using artificial neural networks and genetic algorithms, J. Membr. Sci., 283 (2006) 147-157.
6. M. Cheryan, Ultrafiltration and Microfiltration Handbook, Technomic, Lancaster, Pa., 1998
7. F.E. Siobhan, Development of the MFI-UF in constant-flux filtration, Desalination, 161 (2004) 103-113.
8. K.L. Fox, R.R. Henning and J.H. Reed, A neural-network approach towards intrusion detection, Proc. 13th National Computer Security Conference, 1990.
9. D. Hammerstrom, Neural networks at work, IEEE Spectrum, 1993, pp. 26-53.