Neural networks as a tool for gray box modelling in reactive distillation

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2206 LNCS, Issue , 2001, Pages 576-588

  Dadhe, Kai ^a   Roßmann, Volker ^b   Durmus, Kazim ^a   Engell, Sebastian ^a  

^a TU DORTMUND UNIVERSITY   (Germany)

^b BAYER AG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATION THEORY; DISTILLATION; DISTILLATION COLUMNS; ARTIFICIAL INTELLIGENCE;

APPROXIMATION ABILITY; FEED-FORWARD NETWORK; REACTIVE DISTILLATION COLUMNS; REACTIVE DISTILLATIONS;

RADIAL BASIS FUNCTION NETWORKS;

EID: 52049109752     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45493-4_58     Document Type: Conference Paper

References (11)

1. J. Krafczyk and J. Gmehling. Einsatz von Katalysatorpackungen für die Herstellung von Methylacetat durch reaktive Rektifikation (Use of catalytic packings for the production of methyl acetate by reactive distillation). Chemie Ingenieur Technik, 66(10):1372-1374 (in German), 1994.
2. R. Taylor and R. Krishna. Modelling reactive distillation. Chem. Eng. Sci., 55:5183-5229, 2000. (Pubitemid 32265527)
3. R. Petersen, A. Fredenslund, and P. Rasmussen. Artificial neural networks as a predictive tool for vapor-liquid equilibrium. Comp. & Chem. Engng., 18:S63-S67, 1994.
4. R. Sharma, D. Singhal, R. Ghosh, and A. Dwivedi. Potential application of artificial neural networks to thermodynamics: Vapor liquid equilibrium predictions. Comp. & Chem. Engng., 23:385-390, 1999. (Pubitemid 29179695)
5. E. Alvarez, C. Riverol, J. M. Correa, and J. M. Navaza. Design of a combined mixing rule for the prediction of vapor-liquid equilibria using neural networks. Ind. Eng. Chem. Res., 38:1706-1711, 1999. (Pubitemid 29189588)
6. K. Hornik. Approximation capabilities of multilayer feedforward neural networks. Neural Networks, 4:251-257, 1990.
7. H. Demuth and M. Beale. Neural Network Toolbox. The MathWorks Inc., 1998.
8. J. MacQueen. Some methods for classification and analysis of multivariate observations. In L. M. LeCam and J. Neyman, editors, Proc. Fifth Berkeley Symp. Math. Stat. and Prop., pages 281-297. Berkeley Press, 1967.
9. J. A. Leonard and M. A. Kramer. Radial basis function networks for classifying process faults. IEEE Control Systems, 4, 1991.
10. G. Fernholz, V. Roßmann, S. Engell, and J.-P. Bredehöft. System identification using radial basis function nets for nonlinear model predictive control of a semi-batch reactive distillation column. In Proc. IEE Conference Control 2000, Cambridge, 2000.
11. G. Fernholz, S. Engell, L.-U. Kreul, and A. Górak. Optimal operation of a semi-batch reactive distillation column. Comp. & Chem. Engng., 24:1569-1575, 2000. (Pubitemid 30608399)