Application of optimal RBF neural networks for optimization and characterization of porous materials

Computers and Chemical Engineering

Volumn 29, Issue 10, 2005, Pages 2134-2143

  Shahsavand, A ^a   Ahmadpour, A ^a  

^a FERDOWSI UNIVERSITY OF MASHHAD   (Iran)

Author keywords

Characterization; Neural network; Optimization; Porous materials; Regularization network

Indexed keywords

ACOUSTIC NOISE; CARBON; NUMERICAL METHODS; OPTIMIZATION; POROUS MATERIALS;

CARBON MOLECULAR SIEVES; CHARACTERIZATIONS; OPERATING PARAMETERS; REGULARIZATION NETWORKS;

RADIAL BASIS FUNCTION NETWORKS;

POROUS MEDIUM;

EID: 25844513822     PISSN: 00981354     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compchemeng.2005.07.002     Document Type: Article

References (35)

1. M. Abedinzadegan Abdi, M. Mahdiarfar, A. Jalilian, A. Ahmadpour, and A.R. Mirhabibi Preparation of carbon molecular sieve from a new natural source Proceedings of the American Carbon Society's 25th Conference on Carbon "CARBON 01" Kentucky, USA 2001
2. A. Ahmadpour, M. Abedinzadegan Abdi, M. Mahdiarfar, A.M. Rashidi, A. Jalilian, and A.R. Mirhabibi New carbon molecular sieves for air and hydrocarbon separations Proceedings of the International Conference on Carbon "CARBON 02" Beijing, China 2002
3. Ahmadpour, A. (1997). Fundamental studies on preparation and characterization of carbonaceous adsorbents for natural gas storage. PhD Thesis, University of Queensland, Australia.
4. L.E. Cascarini de Torre, and E.J. Bottani Adsorption energy distribution function Colloids and Surfaces 116 1996 285
5. W.M. Chan, and C.A.O. Nascimento Use of neural networks for modeling of olefin polymerization in high pressure tubular reactors Journal of Applied Polymer Science 53 1994 1277
6. N. Floquet, J.P. Coulomb, and G. André Hydrogen sorption in MCM-41 by neutron diffraction study. Characterization of the porous structure of MCM-41 and the growth mode of the hydrogen confined phases Microporous and Mesoporous Materials 72 1-3 2004 143
7. G.H. Golub, and C.G. Van Loan Matrix Computations 3rd ed. 1996 Johns Hopkins University Press Baltimore
8. G.H. Golub, M. Heath, and G. Wahba Generalized cross validation as a method for choosing a good ridge parameter Technometrics 21 2 1979 215
9. R. Guardani, R.S. Onimaru, and F.C.A. Crespo Neural network model for the on-line monitoring of a crystallization process Braz. J. Chem. Eng. 18 3 2001
10. T.J. Hastie, and R.J. Tibshirani Generalized Additive Models 1st ed. 1990 Chapman and Hall London
11. S. Haykin Neural Networks: A Comprehensive Foundation 2nd ed. 1999 New Jersey Prentice Hall
12. D.M. Himmelblau, and J.C. Hoskins Artificial neural network models of knowledge representation in chemical engineering Computers and Chemical Engineering 12 1988 881
13. K.J. Hunt, D. Sbarbaro, R. Zbikowski, and P.J. Gawthrop Neural networks for control systems - A survey Automatica 28 6 1992 1083
14. I. Iliuta şi, and V. Lavric Two-phase downflow and upflow fixed-bed reactors hydrodynamics modeling using artificial neural network Chem. Ind. 53 6 1999 76
15. J. Jagiello, T.J. Bandosz, and J.A. Schwarz Characterization of microporous carbons using adsorption at near ambient temperatures Langmuire 12 1996 2837
16. C.M. Lastoskie, and K.E. Gubbins Characterization of porous materials using molecular theory and simulation Advances in Chemical Engineering 28 2001 203
17. C.K. Lee, A.S.T. Chiang, and C.S. Tsay The characterization of porous solids from gas adsorption measurements Key Engineering Materials 115 1996 21
18. H.Y. Lin, W. Chen, and A. Tsutsumi Long-term prediction of nonlinear hydrodynamics in bubble columns by using artificial neural networks Chemical Engineering and Processing 42 8/9 2003
19. A. Moussatov, C. Ayrault, and B. Castagnede Porous material characterization - Ultrasonic method for estimation of tortuosity and characteristic length using a barometric chamber Ultrasonic 39 2001 195
20. C.A.O. Nascimento, E. Oliveros, and A.M. Braun Neural network modeling for photochemical processes Chemical Engineering and Processing 33 1994 319
21. C.A.O. Nascimento, R. Guardani, and M. Giulietti Use of neural networks in the analysis of particle size distributions by laser diffraction Powder Technology 90 1997 89
22. C.A.O. Nascimento, R. Giudici, and R. Guardani Neural network based approach for optimization of industrial chemical processes Computers and Chemical Engineering 24 2000 2303
23. J.W. Patrick Porosity in Carbons: Characterization and Applications 1995 Edward Arnold London
24. T. Poggio, and F. Girosi Regularization algorithms for learning that are equivalent to multilayer networks Science 247 1990 978
25. T. Poggio, and F. Girosi Networks for approximation and learning Proceedings of the IEEE 78 1990 1481
26. B.P. Russel, and M.D. LeVan Pore size distribution of BPL activated carbon determined by different methods Carbon 32 1994 845
27. Shahsavand, A., 2000. Optimal and Adaptive Radial Basis Function Neural Networks. PhD. Thesis, University of Surrey, UK.
28. A. Shahsavand A novel method for predicting the optimum width of the isotropic Gaussian Regularization networks Proceedings of the ICNN2003 Minsk, Belarus 2003
29. Shahsavand, A., & Ahmadpour, A. (2005). An optimal regularization network for hypersurface reconstruction. NeuroComputing, submitted for publication.
30. A. Shaikh, and M. Al-Dahhan Development of an artificial neural network correlation for prediction of overall gas holdup in bubble column reactors Chemical Engineering and Processing 42 8/9 2003
31. H.F. Stoeckli, F. Kraehenbuehel, L. Ballerinin, and S. Bernardini Recent developments in the Dubinin equation Carbon 27 1989 125
32. M.V. Szombathely, P. Brauer, and M. Jeroniec The solution of adsorption integral equations by means of regularization method Journal of Computational Chemistry 13 1 1992 17
33. L.A. Tarca, P.A. Grandjean, and F.V. Larachi Reinforcing the phenomenological consistency in artificial neural network modeling of multiphase reactors Chemical Engineering and Processing 42 8/9 2003
34. V. Venkatasubramanian, and K. Chan A neural network methodology for process fault diagnosis AIChE Journal 35 1993
35. K. Watanabe, I. Matsuura, M. Abe, M. Kubota, and D.M. Himmelblau Incipient fault diagnosis of chemical engineering processes via artificial neural networks AIChE Journal 35 11 1989 1803