Dynamic modeling of flux and total hydraulic resistance in nanofiltration treatment of regeneration waste brine using artificial neural networks

Desalination and Water Treatment

Volumn 41, Issue 1-3, 2012, Pages 95-104

  Salehi, Fakhreddin ^a   Razavi, Seyed M A ^a  

^a FERDOWSI UNIVERSITY OF MASHHAD   (Iran)

Author keywords

Hydraulic resistances; Membrane; Simulation; Wastewater treatment

Indexed keywords

EID: 84864110040     PISSN: 19443994     EISSN: 19443986     Source Type: Journal    
DOI: 10.1080/19443994.2012.664683     Document Type: Article

References (26)

1. A. Hinkova, Z. Bubni ́k, P. Kadlec, J. Pridal, Potentials of separation membranes in the sugar industry, Sep. Purif. Technol. 26 (2002) 101-110.
2. S. Cartier, M.A. Theoleyre, M. Decloux, Treatment of sugar decolorizing regeneration waste using nanofiltration, Desalination 113 (1997) 7-17.
3. S. Wadley, C.J. Brouckaert, L.A.D. Baddock, C.A. Buckley, Modelling of nanofiltration applied to the recovery of salt from waste brine at a sugar decolorisation plant, J. Membr. Sci. 102 (1995) 163-175.
4. R.J. Petersen, Composite reverse osmosis and nanofiltration membranes, J. Membr. Sci. 83 (1993) 81-150.
5. N.A. Darwish, N. Hilal, H. Al-Zoubi, A.W. Mohammad, Neural networks simulation of the filtration of sodium chloride and magnesium chloride solutions using nanofiltration membranes, I. Chem. E 85 (2007) 417-430.
6. S.J. Duranceau, Modeling the permeate transient response to perturbations from steady-state in a nanofiltration process, Desalin. Water Treat. 1 (2009) 7-16.
7. I.S. Chang, R. Field, Z. Cui, Limitations of resistance-in-series model for fouling analysis in membrane bioreactors: A cautionary note, Desalin. Water Treat. 8 (2009) 31-36.
8. B. Sarkar, A. Sengupta, S. De, S. DasGupta, Prediction of permeate flux during electric field enhanced cross-flow ultrafiltration- a neural network approach, Sep. Purif. Technol. 65 (2009) 260-268.
9. M. Dornier, M. Decloux, G. Trystram, A. Lebert, Dynamic modeling of crossflow microfiltration using neural networks, J. Membr. Sci. 98 (1995) 263-273.
10. C. Teodosiu, D. Pastravanu, M. Macoveanu, Neural network model for ultrafiltration and backwashing, Water Res. 34 (2000) 4371-4380.
11. T.M. Hwang, Y. Choi, S.H. Nam, S. Lee, H. Oh, K. Hyun, Y. K. Choung, Prediction of membrane fouling rate by neural network modeling, Water Treat. 15 (2010) 134-140.
12. S.M.A. Razavi, S.A. Mortazavi, S.M. Mousavi, Dynamic modeling of milk ultrafiltration by artificial neural network, J. Membr. Sci. 220 (2003) 47-58.
13. A. Abbas, N. Al-Bastaki, Modeling of an RO water desalination unit using neural networks, Chem. Eng. J. 114 (2005) 139-143.
14. W.R. Bowen, M.G. Jones, J.S.T. Welfoo, H.N. Yousef, Predicting salt rejections at nanofiltration membranes using artificial neural networks, Desalination 129 (2000) 147-162.
15. S.C. Tu, V. Ravindran, M. Pirbazari, A pore diffusion transport model for forecasting the performance of membrane processes, J. Membr. Sci. 265 (2005) 9-50.
16. Y. Kaya, H. Barlasa, S. Arayici, Evaluation of fouling mechanisms in the nanofiltration of solutions with high anionic and nonionic surfactant contents using a resistance-in-series model, J. Membr. Sci. 367(1-2) (2011) 45-54.
17. A.K. Pabby, S.S.H. Rizvi and A.M. Sastre, Handbook of Membrane; Separations Chemical, Pharmaceutical, Food, and Biotechnological Applications, Taylor & Francis Group, New York, NY, 2009.
18. S.M.A. Razavi, S.A. Mortazavi, S.M. Mousavi, Application of neural networks for crossflow milk ultrafiltration simulation, Int. Dairy J. 14 (2004) 69-80.
19. N. Hilal, O.O. Ogunbiyi, M. Al-Abri, Neural network modeling for separation of bentonite in tubular ceramic membranes, Desalination 228 (2008) 175-182.
20. Q. Liu, S. Kim, S. Lee, Prediction of microfiltration membrane fouling using artificial neural network models, Sep. Purif. Technol. 70 (2009) 96-102.
21. M. Fathi, M. Mohebbi, S.M.A. Razavi, Application of image analysis and artificial neural networkto predict mass transfer kinetics and color changesof osmotically dehydrated kiwifruit, Food Bioprocess Technol. (2009), doi: 10.1007/s11947-009-0222-y.
22. S.S. Tambe, B.D. Kulkami and P.B. Deshpande, Elements of artificial neural networks with selected applications in chemical engineering, and chemical and biological sciences, Simulation and Advanced Controls, Louisville, KY, 1996.
23. S. Chellam, Artificial neural network model for transient crossflow microfiltration of polydispersed suspensions, J. Membr. Sci. 258 (2005) 35-42.
24. M.T. Hagan, M. Menhaj, Training feed forward networks with the Marquardt algorithm, IEEE. Trans. Neural Network 5 (1994) 989-993.
25. M.P. Rapetto, A. Almqvist, R. Larsson, P.M. Lugt, On the influence of surface roughness on real area of contact in normal, dry, friction free, rough contact by using a neural network, Wear 266(5-6) (2009) 592-595.
26. A. Mohebbi, M. Taheria, A. Soltania, A neural network for predicting saturated liquid density using genetic algorithm for pure and mixed refrigerants, Int. J. Refrig. 31(8) (2008) 1317-1327.