First-principles, data-based, and hybrid modeling and optimization of an industrial hydrocracking unit

Industrial and Engineering Chemistry Research

Volumn 45, Issue 23, 2006, Pages 7807-7816

  Bhutani, N ^a   Rangaiah, G P ^a   Ray, A K ^a  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER SIMULATION; GENETIC ALGORITHMS; MATHEMATICAL MODELS; NEURAL NETWORKS;

DATA BASED MODEL; FIRST PRINCIPLES MODEL (FPM); HYDROCRACKING UNITS;

CHEMICAL ENGINEERING;

CHEMICAL ENGINEERING; COMPUTER SIMULATION; GENETIC ALGORITHMS; MATHEMATICAL MODELS; NEURAL NETWORKS;

EID: 33751565809     PISSN: 08885885     EISSN: None     Source Type: Journal    
DOI: 10.1021/ie060247q     Document Type: Article

References (32)

1. Bhutani, N.; Ray A. K.; Rangaiah, G. P. Modeling, simulation and multi-objective optimization of an industrial hydrocracking unit. Ind. Eng. Chem. Res. 2006, 45, 1354.
2. Mohanty, S.; Saraf, D. N.; Kunzru, D. Modeling of a hydrocracking reactor. Fuel Process. Technol. 1991, 29, 1.
3. Martens, G. G.; Marin, G. B. Kinetics and hydrocracking based on structural classes: Model development and application. AIChE J. 2001, 47, 1607.
4. Mitra, K.; Ghivari, M. Modeling of an industrial wet grinding operation using data-driven techniques. Comput. Chem. Eng. 2006, 30, 508.
5. Torrecilla, J. S.; Arago, J. M.; Palancar, M. C. Modeling the drying of a high-moisture solid with an artificial neural network. Ind. Eng. Chem. Res. 2005, 44, 8057.
6. Duarte, B.; Saruiva, P. M.; Pantelides, C. C. Combining mechanistic and empirical modeling. Int. J. Chem. React. Eng. 2004, 2, 1.
7. Acuña, G.; Cubillos, F.; Thibault, J.; Latrille, E. Comparison of methods for training Grey-Box neural network models. Comput. Chem. Eng. 1999, 23, S561.
8. Molga, E.; Cherbanski, R.; Szpyrkowicz, L. Modeling of an industrial full-scale plant for biological treatment of textile wastewaters: Application of neural networks. Ind. Eng. Chem. Res. 2006, 45, 1039.
9. Bellos, G. D.; Kallinikos, L. E.; Gounaris, C. E.; Papayannakos N. G. Modelling of the performance of industrial HDS reactors using a hybrid neural network approach. Chem. Eng. Process. 2005, 44, 505.
10. Schubert, J.; Simutis, R.; Dors, M.; Havlík, I.; Lübbert, A. Hybrid modeling of yeast production processes - A combination of a priori knowledge on different levels of sophistication. Chem. Eng. Technol. 1994, 17, 10.
11. Georgieva P.; Meireles M. J.; Feyo de Azevedo, S. Knowledge-based hybrid modelling of a batch crystallisation when accounting for nucleation. growth and agglomeration phenomena. Chem. Eng. Sci. 2003, 58, 3699.
12. 2 hydrogenation to methanol. Chem. Eng. J. 2005, 115, 113.
13. Côte, M.; Grandjean, B. P. A.; Lessard, P.; Thibault, J. Dynamic modeling of the activated sludge process: Improving prediction using neural networks. Water Res. 1995, 29, 995.
14. Su, H. T.; McAvoy, T. J.; Werbos, P. Long-Term Predictions of Chemical Processes Using Recurrent Neural Networks: A Parallel Training Approach. Ind. Eng. Chem. Res. 1992, 31, 1338.
15. Van Can, H. J. L.; Hellinga, C.; Luyben, K. C. A. M.; Heijnen, J.; Braake, H. A. B. Strategy for dynamic process modeling based on neural networks and macroscopic balances. AICHE J. 1996, 42, 3403.
16. Hugget, A.; Sébastin, P.; Nadeau, J. P. Global optimization of a dryer by using neural networks and genetic algorithms. AIChE J. 1999, 45 (6), 1227.
17. Psichogios, D. C.; Ungar, L. H. A hybrid neural network-first principles approach to process modeling. AIChE J. 1992, 38 (10), 1499.
18. Thompson, M. L.; Kramer, M. A. Modeling chemical processes using prior knowledge and neural networks. AIChE J. 1994, 40, 1328.
19. Oliveira, R. Combining first principles modeling and artificial neural networks: A general framework. Comput. Chem. Eng. 2004, 28, 755.
20. Pistikopoulous, E. N.; Ierapetritou, M. G. Novel approach for optimal process design under uncertainty. Comput Chem. Eng. 1995, 19, 1089.
21. Mavrovouniotis, M. L.; Chang, S. Hierarchical neural networks for process monitoring. Comput. Chem. Eng. 1992, 16 (4), 347.
22. Zbicinski, I.; Strumillo, P.; Kaminski, W. Hybrid model of thermal drying in a fluidized bed. Comput. Chem. Eng. 1996, 20, 695.
23. Nelson, M. M.; Illingworth, W. T. A Practical Guide to Neural Nets; Addison-Wesley Publishing Co.: Reading, MA, 1991.
24. Hair, J.; Anderson, R.; Tatham, R.; Black, W. Multivariate data analysis, 6th ed.; Pearson Prentice-Hall: Upper Saddle River, NJ, 2006.
25. Papadokonstantakis, S.; Machefer, S.; Schnitzlein, K.; Lygeros, A. I. Variable selection and data preprocessing in NN modelling of complex chemical processes. Comput. Chem. Eng. 2005, 29, 1647.
26. Baughman, D. R.; Liu, Y. A. Neural Networks in Bioprocessing and Chemical Engineering; Academic Press: San Diego, CA, 1995.
27. Pollard, J. F.; Broussard, M. R.; Garrison, D. B.; San, K. Y. Process identification using neural networks. Comput. Chem. Eng. 1992, 16, 253.
28. Haykin, S. Neural Networks: A Comprehensive Foundation, 2nd ed.; Prentice-Hall: New York, 1995.
29. Foresee, F. D.; Hagan, M. T. Gauss-Newton approximation to Bayesian regularization. Proceedings of the 1997 International Joint Conference on Neural Networks; IEEE Press: Piscataway, NJ, 1997; Vol. 3, p 1930.
30. Deb, K. Multi-objective optimization using evolutionary algorithms; Wiley: New York, 2001.
31. Houck, C.; Joines, J.; Kay, M. A genetic algorithm for function optimization: A Matlab implementation, 1995. http://www.ie.ncsu.edu/mirage/ GAToolBox/gaot/ (last accessed Feb 2006).
32. Balasubramaniam, P.; Pushpavanam, S.; Bettina, G.; Balaraman, K. S. Kinetic parameter estimation using genetic algorithms and sequential quadratic programming in a hydrocracker. Ind. Eng. Chem. Res. 2003, 42, 4723-4731.