Neural network based correlation for estimating water permeability constant in RO desalination process under fouling

Desalination

Volumn 345, Issue , 2014, Pages 101-111

  Barello, M ^a   Manca, D ^a   Patel, R ^b   Mujtaba, I M ^b  

^a Dipartimento di Chimica   (Italy)

^b UNIVERSITY OF BRADFORD   (United Kingdom)

Author keywords

Fouling; Neural network modelling; Physical property models; Reverse osmosis; Water permeability elevation

Indexed keywords

DESALINATION; FORECASTING; FOULING; REVERSE OSMOSIS; WATER FILTRATION;

LOW-PRESSURE OPERATIONS; MODEL-BASED OPC; NEURAL NETWORK (NN); NEURAL NETWORK MODELLING; OPERATING PRESSURE; PHYSICAL PROPERTY MODELS; RO DESALINATION; WATER PERMEABILITY;

NEURAL NETWORKS;

ARTIFICIAL NEURAL NETWORK; CORRELATION; DESALINATION; FOULING; NUMERICAL MODEL; PERMEABILITY; PREDICTION;

EID: 84900535017     PISSN: 00119164     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.desal.2014.04.016     Document Type: Article

References (21)

1. Schiffler M. Perspectives and challenges for desalination in the 21st century. Desalination 2004, 165:1-9.
2. Kuriara M., Hiroyuki Y., Takayuki N. High recovery/high pressure membrane for brine con-version SWRO process development and its performance. Desalination 1999, 125:9-15.
3. Whittaker C., Ridgeway H.R., Olson B.H. Evaluation of cleaning strategies for removal of biofilms from reverse osmosis membranes. Appl. Environ. Microbiol. 1984, 48:395-403.
4. Sassi K.M., Mujtaba I.M. Optimal design and operation of reverse osmosis desalination process with membrane fouling. Chem. Eng. J. 2011, 171:582-593.
5. Belfort G., Alexandroviwics G., Marx B. Artificial particulate fouling of hyperfiltration membranes. Desalination 1976, 19:127-138.
6. Jackson J.M., Landolt D. About the mechanism of formation of iron hydroxide fouling layers on reverse osmosis membranes. Desalination 1973, 12:361-378.
7. Gregor H.P., Gregor C.D. Synthetic membrane technology. Sci. Am. 1978, 239:88-101.
8. Jonsson G., Kristen S. Membrane filtration of NSSC-waste liquor. Desalination 1980, 32:327-339.
9. Evangelista F. A short cut method for the design of reverse osmosis desalination plants. Ind. Eng. Process Des. Dev. 1985, 24:221-223.
10. Wilf M., Klinko K. Performances of commercial seawater membranes. Desalination 1994, 96:465-478.
11. Zhu M., El-Halwagi M.M., Al-Ahmad M. Optimal design and scheduling of flexible reverse osmosis networks. J. Membr. Sci. 1997, 129:162-174.
12. Al-Bastaki N., Abbas A. Long-term performance of an industrial water desalination plant. Chem. Eng. Proc. 2004, 43:555-558.
13. Mujtaba I.M., Hussain M.A. Application of Neural Network and Other Learning Technologies in Process Engineering 2001, Imperial College Press, London.
14. Tanvir M.S., Mujtaba I.M. Neural network based correlations for estimating temperature elevation for seawater in MSF desalination process. Desalination 2006, 195:251-272.
15. Aminian A. Prediction of temperature elevation for seawater in multi-stage flash desalination plants using radial basis function neural network. Chem. Eng. J. 2010, 162:552-556.
16. Bhat N., McAvoy T.J. Use of neural nets for dynamic modelling and control of chemical process system. Comput. Chem. Eng. 1990, 14:573-582.
17. Hunt K.J., Sbarbaro D., Zbikowski R., Gawthrop P.J. Neural networks for control system - a survey. Automatica 1992, 28:1083-1112.
18. Psichogios D.C., Ungar L.H. A hybrid neural-network first principles approach to process modeling. Am. Inst. Chem. Eng. J. 1992, 38:1499.
19. Hagan M.T., Demuth H.B., Beale M.N. Neural Network Design 1996, PWS Publishing Company of International Thomson Publishing Inc., Boston.
20. Voros N.G., Maroulis Z.B., Marinos-Kouris D. Salt and water permeability in reverse osmosis membranes. Desalination 1996, 104:141-154.
21. Brian P.L.T. Concentration polarization in reverse osmosis desalination with variable flux and incomplete salt rejection. Ind. Eng. Chem. Found. 1965, 4:439-445.