Part II: Prediction of the dialysis process performance using Artificial Neural Network (ANN)

Desalination

Volumn 265, Issue 1-3, 2011, Pages 11-21

  Godini, H R ^a,b   Ghadrdan, M ^a   Omidkhah, M R ^a   Madaeni, S S ^b  

^a TARBIAT MODARES UNIVERSITY   (Iran)

^b RAZI UNIVERSITY   (Iran)

Author keywords

Artificial Neural Network (ANN); Charged particles; Conductive membrane; Dialysis process; Dynamic prediction

Indexed keywords

ARTIFICIAL NEURAL NETWORK; ARTIFICIAL NEURAL NETWORK MODELS; CHARGED MICELLES; COMPLEX PROCESSES; CONCENTRATION GRADIENTS; CONDUCTIVE MEMBRANES; CRITICAL MICELLE CONCENTRATION (CMC); DETERMINISTIC MODELS; DEVELOPED MODEL; DIFFUSION MECHANISMS; DYNAMIC PREDICTION; ELECTRICAL CHARGES; EXPERIMENTAL DATA; EXPERIMENTAL SYSTEM; FEED CONCENTRATION; LOW CONCENTRATIONS; MAIN PARAMETERS; MICRO-FILTRATION MEMBRANES; NEUTRAL STATE; OPERATING CONDITION; PROCESS PERFORMANCE; SODIUM DODECYL SULPHATE;

CHARGED PARTICLES; CONCENTRATION (PROCESS); CRITICAL MICELLE CONCENTRATION; DIALYSIS; DYNAMICS; FORECASTING; MEMBRANES; MICELLES; MICROFILTRATION; SODIUM;

NEURAL NETWORKS;

ARTIFICIAL NEURAL NETWORK; DIFFUSION; EXPERIMENTAL STUDY; FILTRATION; MASS TRANSFER; MEMBRANE; NUMERICAL MODEL; PERFORMANCE ASSESSMENT; PREDICTION;

EID: 78349313205     PISSN: 00119164     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.desal.2010.04.039     Document Type: Article

References (24)

1. Madaeni S.S., Godini H.R. Transfer characterization of charged particles through charged conductive membrane via dialysis process. J. Porous Mater. 2008, 15(4):467-474.
2. Bandini S. Modelling the mechanism of charge formation in NF membranes: theory and application. J. Membr. Sci. 2005, 264:75-86.
3. Samuel de Lint W.B., Benes N.E. Separation properties of γ-alumina nanofiltration membranes, compared to charge regulation model predictions. J. Membr. Sci. 2005, 248:149-159.
4. Samuel de Lint W.B., Benes N.E. Predictive charge-regulation transport model for nanofiltration, from the theory of irreversible processes. J. Membr. Sci. 2004, 243:365-377.
5. Szymczyk A., Fatin-Rouge N., Fievet P., Ramseyer C., Vidonne A. Identification of dielectric effects in nanofiltration of metallic salts. J. Membr. Sci. 2007, 287:102-110.
6. Kim M., Zydney A.L. Particle-particle interactions during normal flow filtration: model simulations. Chem. Eng. Sci. 2005, 60:4073-4082.
7. Weng Y.H., Li K.C., Chaung-Hsieh L.H., Huang C.P. Removal of humic substances (HS) from water by electro-micro-filtration (EMF). Water Res. 2006, 40:1783-1794.
8. Nghiem L.D., Schafer A.I., Elimelech M. Role of electrostatic interactions in the retention of pharmaceutically active contaminants by a loose nano-filtration membrane. J. Membr. Sci. 2006, 286:52-59.
9. Takagi R. Equation of rejection curve for membranes with high charge density. Desalination 15 December 2008, 233:267-276.
10. Lin Y.T., Sung M., Sanders P.F., Marinucci A., Huang C.P. Separation of nano-sized colloidal particles using cross-flow electro-filtration. Sep. Purif. Technol. 2007, 58:138-147.
11. Vasan S.S., Field R.W., Cui Z. A Maxwell-Stefan-Gouy-Debye model of the concentration profile of a charged solute in the polarization. Desalination 2006, 192:356-363.
12. Ahadian S., Moradian S., Sharif F., Amani M., Mohseni M. Prediction of time of capillary rise in porous media using artificial neural network (ANN). I.J.Ch.E. 2007, 26:71-84.
13. Marry V., Dufreche J.F., Jardat M., Meriguet G., Turq P., Grun F. Dynamics and transport in charged porous media. Colloids Surf., A 2003, 222:147-153.
14. Dornier M., Decloux M., Trystram G., Lebert A. Interest of neural networks for the optimization of the crossflow filtration process. Lebensm.-Wiss. U.-Technol. 1995, 28:300-309.
15. Niemi H., Bulsari A., Palosaari S. Simulation of membrane separation by neural networks. J. Membr. Sci. 1995, 102:85-191.
16. Piron E., Latrille E., Rene' F. Application of artificial neural networks for crossflow microfiltration modelling: "black box" and semiphysical approach. Comp. Chem. Eng. 1997, 21:1021-1030.
17. Fu R.Q., Xu T.W., Pan Z.X. Modelling of the adsorption of bovine serum albumin on porous polyethylene membrane by back-propagation artificial neural network. J. Membr. Sci. 2005, 251:137-144.
18. Yu Z., Taylor J.S., Chellam S. Predicting RO/NF water quality by modified solution diffusion model and artificial neural networks. J. Membr. Sci. 2005, 263:38-46.
19. Madaeni S.S. Preparation and properties of composite membrane composed of non-conductive membranes ans polymers. Indian J. Chem. Technol. January 2006, 13:65-70.
20. Salehi E., Madaeni S.S. Influence of conductive surface on adsorption behavior of ultrafiltration membrane. Appl. Surf. Sci. 2010, 256:3010-3017.
21. Bowen W.R., Jones M.G., Yousef H.N.S. Prediction of the rate of cross flow membrane ultrafiltration of colloids: a neural network approach. Chem. Eng. Sci. 1998, 53:3793-3802.
22. Tambe S.S., Kulkarni B.D., Deshpande P.B. Elements of Artificial Neural Networks with Selected Applications in Chemical Engineering. Chemical and Biological Sciences, Simulation and Advanced Controls, Louisville-KY, U.S.A. 1996.
23. Hornik K., Stinchcombe M., White H. Multilayer feed forward networks are universal approximators. Neural Networks 1989, 2:359-366.
24. Bowen W.R., Jenner F. Theoretical, descriptions of membrane filtration of colloids and fine particles: an assessment and review. Adv. Colloid Interface Sci. 1995, 56:141-200.