Membrane permeate flux and rejection factor prediction using intelligent systems

Desalination

Volumn 284, Issue , 2012, Pages 92-99

  Sargolzaei, J ^a   Haghighi Asl, M ^a   Hedayati Moghaddam, A ^a  

^a FERDOWSI UNIVERSITY OF MASHHAD   (Iran)

Author keywords

Adaptive neuro fuzzy inference system (ANFIS); Backpropagation neural network (BPNN); Membrane; Radial basis function (RBF); Simulation

Indexed keywords

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM; BACK-PROPAGATION ARTIFICIAL NEURAL NETWORK; BACKPROPAGATION NEURAL NETWORK (BPNN); CHEMICAL PROCESS; COD REMOVAL; HIDDEN LAYERS; INPUT VECTOR; MEMBRANE MODULE; MEMBRANE PERFORMANCE; OPERATIVE PARAMETERS; OPTIMUM CONDITIONS; PERFORMANCE PREDICTION; PERMEATE FLUX; POLYETHERSULFONE MEMBRANE; RADIAL BASIS FUNCTIONS; SIMULATION; SIMULATION METHODS;

BACKPROPAGATION; ETHERS; FORECASTING; FUZZY INFERENCE; FUZZY SYSTEMS; INTELLIGENT SYSTEMS; MEMBRANES; RADIAL BASIS FUNCTION NETWORKS; WASTEWATER;

NEURAL NETWORKS;

ARTIFICIAL NEURAL NETWORK; BACK PROPAGATION; CHEMICAL OXYGEN DEMAND; CONCENTRATION (COMPOSITION); MEMBRANE; PH; PREDICTION; STARCH; WASTEWATER;

EID: 83255185122     PISSN: 00119164     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.desal.2011.08.041     Document Type: Article

References (27)

1. Al-Abri M., Hilal N. Artificial neural network simulation of combined humic substance coagulation and membrane filtration. Chem. Eng. J. 2008, 141:27-34.
2. Kalogirou S.A. Artificial neural networks in renewable energy systems applications: a review. Renewable Sustainable Energy Rev. 2001, 5:373-401.
3. Lee W.Y., Park G.G., Yang T.H., Yoon Y.G., Kim C.S. Empirical modeling of polymer electrolyte membrane fuel cell performance using artificial neural networks. Int. J. Hydrogen Energy 2004, 29:961-966.
4. Lee W.Y., Li X.Y., Yang T.H., Kim C.S. Neural network based fuel cell modeling and simulation. Proceedings of Fuel Cell Symposium, Korea 2001, 43-48.
5. Curcio S., Calabrò V., Iorio G. Reduction and control of flux decline in cross-flow membrane processes modeled by artificial neural networks. J. Membr. Sci. 2006, 286:125-132.
6. Chellam S. Artificial neural network model for transient crossflow microfiltration of polydispersed suspensions. J. Membr. Sci. 2005, 258:35-42.
7. Godini H.R., Ghadrdan M., Omidkhah M.R., Madaeni S.S. Part II: prediction of the dialysis process performance using Artificial Neural Network (ANN). Desalination 2011, 265:11-21.
8. Guadix A., Zapata J.E., Almecija M.C., Guadix E.M. Predicting the flux decline in milk cross-flow ceramic ultrafiltration by artificial neural networks. Desalination 2010, 250:1118-1120.
9. Lee Y.G., Lee Y.S., Jeon J.J., Lee S., Yang D.R., Kim I.S., Kim J.H. Artificial neural network model for optimizing operation of a seawater reverse osmosis desalination plant. Desalination 2009, 247:180-189.
10. Omata K., Masuda A., Ishii H., Suzuki H., Yamada M. Artificial neural network aided screening for membrane disc catalysts for oxidative reforming of methane. Appl. Catal. A 2009, 362:14-19.
11. Zhao Y., 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.
12. Teodosiu C., Pastravanu O., Macoveanu M. Neural network models for ultrafiltration and backwashing. Water. Res. 2000, 34:4371-4380.
13. Sargolzaei J., Khoshnoodi M., Saghatoleslami N., Mousavi M. Fuzzy inference system to modeling of crossflow milk ultrafiltration. Appl. Soft Comput. 2008, 8:456-465.
14. Sargolzaei J., Kianifar A. Modeling and simulation of wind turbine Savonius rotors using artificial neural networks for estimation of the power ratio and torque. Simul. Modell. Pract. Theory 2009, 17:1290-1298.
15. Iyatomi H., Hagiwara M. Scenery image recognition and interpretation using fuzzy inference neural networks. Pattern Recognit. 2002, 35:1793-1806.
16. Iyatomi H., Hagiwara M. Knowledge extraction from scenery images and recognition using fuzzy inference neural networks. Electron. Commun. Jpn. (Part II: Electron.) 2003, 86:82-90.
17. Prádanos P., Arribas J.I., Hernández A. Mass transfer coefficient and retention of PEGs in low pressure cross-flow ultrafiltration through asymmetric membranes. J. Membr. Sci. 1995, 99:1-20.
18. García-Pedrajas N., Ortiz-Boyer D., Hervás-Martínez C. An alternative approach for neural network evolution with a genetic algorithm: crossover by combinatorial optimization. Neural Netw. 2006, 19:514-528.
19. Surya Kalavathi M., Ravindranath Reddy B., Singh B.P. Modeling transformer internal short circuit faults using neural network techniques. Electrical Insulation and Dielectric Phenomena, Annual Report Conference 2005, 601-604.
20. Park J., Sandberg I.W. Universal approximation using radial-basis-function networks. Neural Comput. 1991, 3:246-257.
21. Chen S., Cowan C.F.N., Grant P.M. Orthogonal least squares learning algorithm for radial basis function networks. IEEE Trans. Neural Netw. 1991, 2:302-309.
22. Bors A.G., Gabbouj M. Minimal topology for a radial basis functions neural network for pattern classification. Digital Signal Process. 1994, 4:173-188.
23. Poggio T., Girosi F. Networks for approximation and learning. Proc. IEEE 1990, 78:1481-1497.
24. Bors A.G., Pitas I. Median radial basis function neural network. IEEE Trans. Neural Netw. 1996, 7:1351-1364.
25. Madaeni S.S., Kurdian A.R. Fuzzy modeling and hybrid genetic algorithm optimization of virus removal from water using microfiltration membrane. Chemical Engineering Research & Design 89 2011, 4A:456-470.
26. Sargolzaei J. Comparative study of artificial neural nets (ANN) and statistical methods for predicting the performance of ultrafiltration process in the milk industry. Iran. J. Chem. Chem. Eng. 2006, 25:67-76.
27. Richard Bowen W., Jones M.G., Yousef H.N.S. Prediction of the rate of crossflow membrane ultrafiltration of colloids: a neural network approach. Chem. Eng. Sci. 1998, 53:3793-3802.