Rejection and modelling of sulphate and potassium salts by nanofiltration membranes: neural network and Spiegler-Kedem model

Desalination

Volumn 206, Issue 1-3, 2007, Pages 42-60

  Al Zoubi, H ^a   Hilal, N ^a   Darwish, N A ^b   Mohammad, A W ^c  

^a UNIVERSITY OF NOTTINGHAM   (United Kingdom)

^b KHALIFA UNIVERSITY   (United Arab Emirates)

^c UNIVERSITI KEBANGSAAN MALAYSIA   (Malaysia)

Author keywords

Artificial neuron networks; Membrane; Nanofiltration; Permeate flux; Pre treatment; Salt rejections; Seawater; Spiegler Kedem model

Indexed keywords

COMPUTER SIMULATION; INTERPOLATION; MATHEMATICAL MODELS; MECHANICAL PERMEABILITY; NEURAL NETWORKS; SALTS;

POTASSIUM SALTS; SPIEGLER-KEDEM MODEL; SULPHATE SALTS;

NANOFILTRATION;

COMPUTER SIMULATION; INTERPOLATION; MATHEMATICAL MODELS; MECHANICAL PERMEABILITY; NANOFILTRATION; NEURAL NETWORKS; SALTS;

ARTIFICIAL NEURAL NETWORK; FILTRATION; MEMBRANE; MODELING; POTASSIUM; SALT; SULFATE;

EID: 33846786458     PISSN: 00119164     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.desal.2006.02.060     Document Type: Article

References (44)

1. Hassan A., Al-Sofi M., Al-Amoudi A., Jamaluddin A., Farooque A., Rowaili A., Dalvi A., Kither N., Mustafa G., and Al-Tisan I. A new approach to thermal seawater desalination processes using nanofiltration membranes (Part 1). Desalination 118 (1998) 35-51
2. Al-Sofi M., Hassan A., Mustafa G., Dalvi A., and Kither M. Nanofiltration as a means of achieving higher TBT of ≥120°C in MSF. Desalination 118 (1998) 123-129
3. Al-Sofi M. Seawater desalination - SWCC experience and vision. Desalination 135 (2001) 121-139
4. Hassan A., Farooque A., Jamaluddin A., Al-Amoudi A., Al-Sofi M., Al-Rubaian A., Kither N., Al-Tisan I., and Rowaili A. A demonstration plant based on the new NF-SWRO process. Desalination 131 (2000) 157-171
5. Criscuoli A., and Drioli E. Energetic and exergetic analysis of an integrated membrane desalination system. Desalination 124 (1999) 243-249
6. Eriksson P. Nanofiltration extends the range of membrane filtration. Environ.l Progress 7 (1988) 58-61
7. Hilal N., Al-Zoubi H., Darwish N., Mohammed A., and Abu Arabi M. A comprehensive review of nanofiltration membranes: treatment, pretreatment, modelling, and atomic force microscopy. Desalination 170 (2004) 281-308
8. Peeters J., Boom J., Mulder M., and Strathmann H. Retention measurements of nanofiltration membranes with electrolyte solutions. J. Membr. Sci. 145 (1998) 199-209
9. Schaep J., Van der Bruggen B., Vandecasteele C., and Wilms D. Influence of ion size and charge in nanofiltration. Separ. Purif. Technol. 14 (1998) 155-162
10. Afonso M., Hagmeyer G., and Gimbel R. Streaming potential measurements to assess the variation of nanofiltration membranes surface charge with the concentration of salt solutions. Separ. Purif. Technol. 22-23 (2001) 529-541
11. Hagmeyer G., and Gimbel R. Modelling the salt rejection of nanofiltration membranes for ternary ion mixtures and for single salts at different pH values. Desalination 117 (1998) 247-256
12. Bowen R., Mohammad A., and Hilal N. Characterisation of nanofiltration membranes for predictive purposes use of salts, uncharged solutes and atomic force microscopy. J. Membr. Sci. 126 (1997) 91-105
13. Schaep J., Vandecasteele C., Mohammad W., and Bowen R. Modelling the retention of ionic components for different nanofiltration membranes. Separ. Purif. Technol. 22-23 (2001) 169-179
14. Andrew B. Sulfate removal by nanofiltration. Filtr. Separ. 30 (2001) 18-20
15. Van der Bruggen B., and Vandecasteele C. Removal of pollutants from surface water and groundwater by nanofiltration: overview of possible applications in the drinking water industry. Environ. Poll. 122 (2003) 435-445
16. Bowen W., and Mukhtar H. Characterisation and prediction of separation performance of nanofiltration membranes. J. Membr. Sci. 112 (1996) 263-274
17. Bowen W., and Mohammad A. Diafiltration of dye/salt solution by nanofiltration: prediction and optimization. AIChE J. 44 (1998) 1799-1812
18. Bandini S., and Vezzani D. Nanofiltration modeling: the role of dielectric exclusion in membrane characterization. Chem. Eng. Sci. 58 (2003) 3303-3326
19. Straatsma J., Bargeman G., van der Horst H., and Wesselingh J.A. Can nanofiltration be fully predicted by a model?. J. Membr. Sci. 198 (2002) 273-284
20. Bowen W., Jones M., Welfoo J., and Yousef H. Predicting salt rejections at nanofiltration membranes using artificial neural networks. Desalination 129 (2000) 147-162
21. Kedem O., and Katchalsky A. Permeability of composite membranes, Part I: Electric current, volume flow and flow of solute through membranes. Trans. Faraday Soc. 59 (1963) 1918-1930
22. Spiegler K.S., and Kedem O. Thermodynamics of hyperfiltration (reverse osmosis): Criteria for efficient membranes. Desalination 1 (1966) 311-326
23. Schirg P., and Widmer F. Characterisation of nanofiltration membranes for the separation of aqueous dye-salt solutions. Desalination 89 (1992) 89
24. Levenstein R., Hasson D., and Semiat R. Utilisation of the Donnan effect for improving electrolyte separation with nanofiltration membranes. J. Membr. Sci. 116 (1996) 77-92
25. Pontalier P., Ismail A., and Ghoul M. Specific model for nanofiltration. J. Food Eng. 40 (1999) 145-151
26. Koyuncu I., and Yazgan M. Application of nanofiltration and reverse osmosis membranes to the salty and polluted surface water. J. Environ. Sci. Health A36 7 (2000) 1321-1333
27. Diawara C., Lo S., Rumeau M., Pontie M., and Sarr O. A phenomenological mass transfer approach in nanofiltration of halide ions for a selective defluorination of brackish drinking water. J. Membr. Sci. 219 (2003) 103-112
28. Hilal N., Al-Zoubi H., Mohammed A.W., and Darwish N.A. Nanofiltration of highly concentrated salt solutions up to seawater salinity. Desalination 184 (2005) 1295-1306
29. Xu X., and Spencer H.G. Dye-salt separations by nanofiltration using weak acid polyelectrolyte membranes. Desalination 114 (1997) 129-137
30. Dornier M., Decloux M., Trystram G., and Lebert A. Dynamic modeling of crossflow microfiltration using neural networks. J. Membr. Sci. 98 (1995) 263-273
31. Piron E., Latrille E., and Ren F. Application of artificial neural networks for cross flow microfiltration modeling: "black-box" and semi-physical approaches. Comp. Chem. Eng. 21 (1997) 1021-1030
32. Hamachi M., Cabassud M., Davin A., and Peuchot M.M. Dynamic modelling of crossflow microfiltration of bentonite suspension using recurrent neural networks. Chem. Eng. Process. 38 (1999) 203-210
33. Bowen W.R., Jones M.G., and Yousef H.N. Dynamic ultrafiltration of proteins - A neural network approach. J. Membr. Sci. 146 (1998) 225-235
34. Bowen W.R., Jones M.G., and Yousef H.N.S. Prediction of the rate of crossflow membrane ultrafiltration of colloids: A neural network approach. Chem. Eng. Sci. 53 (1998) 3793
35. Delgrange N., Cabassud C., Cabassud M., Durand-Bourlie L., and Lainé J.M. Modelling of ultrafiltration fouling by neural network. Desalination 118 (1998) 213
36. Delgrange N., Cabassud C., Cabassud M., Durand-Bourlie L., and Lainé J.M. Neural network for prediction of ultrafiltration transmembane pressure - application to drinking water. J. Membr. Sci. 150 (1998) 111-123
37. Teodosiu C., Pastravanu O., and Macoceanu M. Neural network model for ultrafiltration and backwashing. Wat. Res. 34 (2000) 4371-4380
38. Bowen W.R., Jones M.G., Welfoo J.S., and Yousef H.N.S. Predicting salt rejections at nanofiltration membranes using artificial neural networks. Desalination 129 (2000) 147-162
39. Nakao S., and Kimura S. Chem. Eng. Japan 14 (1981) 32
40. Schweitzer P.A. Handbook of Separation Techniques for Chemical Engineers. 2nd ed. (1988), Mc Graw-Hill
41. Tambe S.S., Kulkami B.D., and Deshpande P.B. Elements of Artificial Neural Networks with Selected Applications in Chemical Engineering, and Chemical and Biological Sciences (1996), Simulation and Advanced Controls, Ltd., Louisville, KY, USA
42. Rumelhart D.E., Hinton G.E., and Williams R.J. Learning representations by back-propagating errors. Nature 323 (1986) 533-536
43. Hilal N., Al-Zoubi H., Darwish N.A., and Mohammad A.W. Characterisation of nanofiltration membranes using atomic force microscopy. Desalination 177 (2005) 187-199
44. N.A. Darwish, H. Al-Zoubi, N. Hilal and A.W. Mohammad, Neural networks simulation of the filtration of sodium chloride and magnesium chloride solutions using nanofiltration membranes, J. Chem. Eng. Res. Design, submitted for publication.