Model based control of a yeast fermentation bioreactor using optimally designed artificial neural networks

Chemical Engineering Journal

Volumn 127, Issue 1-3, 2007, Pages 95-109

  Nagy, Zoltan Kalman ^a  

^a LOUGHBOROUGH UNIVERSITY   (United Kingdom)

Author keywords

Artificial neural networks; Fermentation bioreactor model; Model based control

Indexed keywords

COMPUTER SIMULATION; FERMENTATION; MATHEMATICAL MODELS; NEURAL NETWORKS; TEMPERATURE CONTROL; YEAST;

FERMENTATION BIOREACTOR MODEL; MODEL BASED CONTROL; NEURAL NETWORK MODEL;

BIOREACTORS;

BIOREACTORS; COMPUTER SIMULATION; FERMENTATION; MATHEMATICAL MODELS; NEURAL NETWORKS; TEMPERATURE CONTROL; YEAST;

EID: 33846893475     PISSN: 13858947     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cej.2006.10.015     Document Type: Article

References (29)

1. Garcia C.E., Prett D.M., and Morari M. Model predictive control: theory and practice-a survey. Automatica 25 (1989) 335-348
2. Hunt K.J., Sbarbaro D., Zbikowski R., and Gawthrop P.J. Neural networks for control systems-a survey. Automatica 28 6 (1992) 1083-1112
3. Aldrich C., and van Deventer J. Comparison of different artificial neural nets for the detection and location of gross errors in process systems. Ind. Eng. Chem. Res. 34 1 (1995) 216-224
4. Ungar L.H., Powel B., and Kamens S.N. Adaptive network for fault diagnosis and process control. Comput. Chem. Eng. 14 (1990) 561-572
5. Baughman D.R., and Liu Y.A. An expert network for predictive modeling and optimal design of extractive bioseparations in aqueous two-phase systems. Ind. Eng. Chem. Res. 33 11 (1994) 2668-2687
6. Pollard J.F., Broussard M.R., Garrison D.B., and San K.Y. Process identification using neural networks. Comput. Chem. Eng. 16 4 (1992) 253-270
7. Bhat N.V., and McAvoy T.J. Use of neural nets for dynamic modeling and control of chemical process systems. Comput. Chem. Eng. 14 4-5 (1990) 573-585
8. Cheng Y., Karjala T.W., and Himmelblau D.M. Identification of nonlinear dynamic processes with unknown and variable dead time using an internal recurrent neural network. Ind. Eng. Chem. Res. 34 5 (1995) 1735-1742
9. Joseph B., and Hanratty F.W. Predictive control of quality in a batch manufacturing process using artificial neural network models. Ind. Eng. Chem. Res. 32 9 (1993) 1951-1961
10. Hoskins J.C., and Himmelblau D.M. Process control via artificial neural networks and reinforcement learning. Comput. Chem. Eng. 16 4 (1992) 241-251
11. Willis M.J., Montague G.A., Di Massimo C., Tham M.T., and Morris A.J. Artificial neural networks in process estimation and control. Automatica 28 6 (1992) 1181-1187
12. Whiteley J.R., and Davis J.F. Knowledge-based interpretation of sensor patterns. Comput. Chem. Eng. 16 4 (1992) 329-346
13. Kavuri S.N., and Venkatasubramanian V. Combining pattern classification and assumption-based techniques for process fault diagnosis. Comput. Chem. Eng. 16 4 (1992) 299-312
14. Hernandez E., and Arkun Y. Study of the control-relevant properties of backpropagation neural network models of nonlinear dynamic systems. Comput. Chem. Eng. 16 4 (1992) 227-240
15. Chen J., and Bruns D.P. WaveARX neural network development for system identification using a systematic design synthesis. Ind. Eng. Chem. Res. 34 12 (1995) 4420-4435
16. Kramer M.A. Autoassociative neural networks. Comput. Chem. Eng. 16 4 (1992) 313-328
17. Mavrovouniotis M.L., and Chang S. Hierarchical neural networks. Comput. Chem. Eng. 16 4 (1992) 347-369
18. Bhat N.V., and McAvoy T.J. Determining model structure for neural models by network stripping. Comput. Chem. Eng. 16 4 (1992) 271-281
19. Wang F.-S., and Sheu J.-W. Multiobjective parameter estimation problems of fermentation processes using a high ethanol tolerance yeast. Chem. Eng. Sci. 55 (2000) 3685-3695
20. Zhu G.-Y., Zamamiri A., Henson M.A., and Hjorts M.A. Model predictive control of continuous yeast bioreactors using cell population balance models. Chem. Eng. Sci. 55 (2000) 6155-6167
21. Di Massimo C., Montague G.A., Willis M.J., Tham M.T., and Morris A.J. Towards improved penicillin fermentation via artificial neural networks. Comput. Chem. Eng. 16 4 (1992) 283-291
22. H. Demuth, M. Beale, Neural Network Toolbox User's Guide, The MathWorks Inc., 1993.
23. Roels J.A. Mathematical models and the design of biochemical reactors. J. Chem. Technol. Biotechnol. 32 (1982) 59-72
24. Volesky B., Yerushalmi L., and Luong J.H.T. Metabolic-heat relation for aerobic yeast respiration and fermentation. J. Chem. Technol. Biotechnol. 32 (1982) 650-659
25. 2 gas effects in a packed bed reactor. J. Chem. Technol. Biotechnol. 32 (1982) 959-967
26. Aiba S., Shoda M., and Nagatani M. Kinetics of product inhibition in alcoholic fermentation. Biotechnol. Bioeng. 10 (1968) 846-864
27. B. Sevella, Bioengeneering Operations, Technical University of Budapest, Tankonykiado, Budapest, 1992.
28. Godia F., Casas C., and Sola C. Batch alcoholic fermentation modelling by simultaneous integration of growth and fermentation equations. J. Chem. Technol. Biotechnol. 41 (1988) 155-165
29. L.K. Hansen, M.W. Pedersen, Controlled growth of cascade correlation nets, in: M. Marino, P.G. Morasso (Eds.), Proceedings ICANN'94, Sorrento, Italy, 1994, pp. 797-800.