A hybrid recurrent neural network model for yeast production monitoring and control in a wine base medium

Journal of Biotechnology

Volumn 55, Issue 3, 1997, Pages 157-169

  Teissier, P ^a   Perret, B ^a   Latrille, E ^a   Barillere, J M ^b   Corrieu, G ^a  

^a AGROPARISTECH   (France)

^b Mumm Perrier Jouet Vignobles R   (France)

Author keywords

Batch culture; Indirect measurement; Process control; Recurrent neural network; State estimation; State prediction; Tirage yeast inoculum; Wine

Indexed keywords

BATCH CELL CULTURE; CARBON DIOXIDE; COMPOSITION; COMPUTER SIMULATION; ETHANOL; NEURAL NETWORKS; PROCESS CONTROL; STATE ESTIMATION; SUGARS; VOLUME MEASUREMENT; WINE; YEAST;

INDIRECT MEASUREMENT; RECURRENT NEURAL NETWORK; TIRAGE YEAST INOCULUM;

BIOTECHNOLOGY;

CARBON DIOXIDE;

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; CULTURE MEDIUM; FUNGUS CULTURE; FUNGUS GROWTH; HYBRID; MEASUREMENT; NONHUMAN; PRIORITY JOURNAL; PROCESS CONTROL; SACCHAROMYCES; TEMPERATURE; WINE; YEAST;

EID: 0031552599     PISSN: 01681656     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0168-1656(97)00065-5     Document Type: Article

References (37)

1. Barron, A.R., 1994. Approximation and estimation bounds for artificial neural networks. Machine Learning 14, 115-133.
2. Bastin, G., Dochain, D., 1990. Process Measurement and Control 1. On Line Estimation and Adaptative Control of Bioreactors. Elsevier, Amsterdam.
3. Billings, S.A., Jamaluddin, H.B., Chen, S., 1992. Properties of neural networks with applications to modelling non-linear dynamical systems.. Int. J. Control. 55, 193-224.
4. Bishop, C.M., 1994. Neural networks and their applications. Rev. Sci. Inst. 65, 1803-1832.
5. Bulsari, Z., Saxen, H., 1994. Using feed-forward neural networks for estimation of microbial concentration in a simulated biochemical process. Biosens. Bioelec. 9, 105-109.
6. Caro, I., Perez, L., Cantero, D., 1991. Development of a kinetic model for the alcoholic fermentation of must. Biotech. Bioeng. 38, 742-748.
7. Chattaway, T., Demain, A.L., Stephanopoulos, G.N., 1992. Use of various measurements for biomass estimation. Biotechnol. Prog. 8, 81-84.
8. Clarke, D.J., Blake-Coleman, B., 1986. Monitoring reactor biomass. Trends Biotech. 4, 173-178.
9. Cléran, Y., Thibault, J., Chéruy, A., Corrieu, G., 1991. Comparison of prediction performances between models obtained by the Group Method of Data Handling and neural networks for the alcoholic fermentation rate in enology. J. Ferm. Biotechnol. 71, 356-362.
10. Collins, M., 1993. Empiricism strikes back: neural networks in biotechnology. Bio/technol. 11, 163-166.
11. Di Massimo, C., Lant, P.A., Saunders, A., Montague, A., Tham, M.T., Morris, A.J., 1992. Bioprocess application of model-based estimation techniques. J. Chem. Tech. Biotechnol. 53, 265-277.
12. El Haloui, N.E., Picque, D., Corrieu, G., 1987. Mesures physiques permettant le suivi biologique de la fermentation alcoolique en oenologie. Sci. des aliments 7, 241-265.
13. El Haloui, N.E., Cléran, Y., Sablayrolles, J.M., Barre, P., Corrieu, G., 1988a. Suivi et contrôle de la fermentation alcoolique en oenologie. Essais à l'échelle industrielle. Rev. Fr. Oenol. 115, 12-15.
14. El Haloui, N.E., Picque, D., Corrieu, G., 1988b. Alcoholic fermentation in wine making: on-line measurement of density and carbon dioxyde evolution. J. Food Eng. 8, 17-30.
15. Funahashi, K.I., Nakamura, Y., 1993. Approximation of dynamical systems by continuous time recurrent neural networks. Neural Networks 6, 801-806.
16. Gerland, C., Sablayrolles, J.M., Barre, P., 1995. Production de levains de tirage en Champagne. Influence de quelques variables opératoires. Revue Française d'Oenologie 151, 5-12.
17. Holmberg, A., 1982. On the practical identifiability of microbial growth models incorporating Michaelis-Menten type nonlinearities. Math. Biosci. 62, 23-43.
18. Karim, M.N., Rivera, S.L., 1992. Artificial neural networks in bioprocess state estimation. Adv. Biochem. Eng. Biotechnol. 46, 1-33.
19. Latrille, E., Corrieu, G., Thibault, J., 1994. Neural network models for final process time determination in fermented milk production. J. Comp. Chem. Eng. 8, 1171-1181.
20. Lim, H.C., Lee, K.S., 1991. Control of bioreactor systems. In: Biotechnology Measuring Modelling and Control, vol. 4, 2nd ed. K. Schugerl, Germany, pp. 511-560.
21. Linko, P., Zhu, Y.H., 1992. Neural network in bioengineering. Kemia-Kemi. 19, 215-220.
22. Montague, A., 1994. Neural networks in biotechnology. Tibtech. 12, 312-323.
23. Moulin, J.P., 1987. Champagne: the method of production and the origin of the quality of this French wine. Proceeding of 6th Australian Wine Industry Technical Conference, 218-223.
24. Narendra, K.S., Parthasarathy, K., 1990. Identification and control of dynamical systems using neural networks. IEEE Trans. Neural Networks 1, 4-27.
25. Nerrand, O., Roussel-Ragot, P., Personnaz, L., Dreyfus, G., Marcos, S., 1993. Neural networks and non-linear adaptive filtering: unifying concepts and new algorithms. Neural Comput. 5, 165-199.
26. Oner, M.P., Erickson, L.E., Yang, S., 1984. Estimation of yield maintenance and product formation kinetic parameters in anaerobic fermentation. Biotechnol. Bioeng. 26, 1436-1444.
27. Schubert, J., Simutis, R., Dors, M., Havlik, I., Lubbert, A., 1994. Bioprocess optimisation and control: application of hybrid modelling. J. Biotechnol. 35, 51-68.
28. Sjöberg, J., Zhang, Q., Ljung, L., Benveniste, A., Deylon, B., Glorennec, P.Y., Hjalmarsson, H., Juditsky, A., 1995. Nonlinear black-box modelling in system identification: a unified overview. Automatica 31, 1691-1724.
29. Sonnleitner, B., Locher, G., Fiechter, A., 1992. Biomass determination.. J. Biotechnol 25, 5-22.
30. Spriet, J.A., 1982. Modelling the growth of microorganisms. In: Rinaldi, S. (Ed.), A critical apparaisal environmental system analysis and management, IFIP, pp. 451-465.
31. Stassi, P., Fehring, J.F., Ball, C.B., Goetzke, G.P., Ryder, D.S., 1995. Optimization of fermentor operations using a fermentor instrumentation system. MBAA Tech. Quarterly 32, 57-65.
32. Su, H.T., McAvoy, T.J., Werbos, P., 1992. Long-term predictions of chemical processes using recurrent neural networks: a parallel training approach. Ind. Eng. Chem. Res. 31, 1338-1352.
33. Teissier, P., Perret, B., Latrille, E., Corrieu, G., Barillère, J.M., 1996. Yeast concentration estimation and prediction with static and dynamic neural networks models in batch cultures. Bioproc. Eng. 14, 231-238.
34. Thibault, J., Van Breusegem, V., Cheruy, A., 1990. On-line prediction of fermentation variables using neural networks. Biotechnol. Bioeng. 36, 1041-1048.
35. Thibault, J., 1991. Feedforward neural networks for the identification of dynamic processes. Chem. Eng. Comm. 105, 109-128.
36. Thibault, J., Van Breusegem, V., 1992. Modelling and on-line optimization of a fedbatch fermentation using neural networks. Bioauto. 12, 79-100.
37. Yamane, T., Shimizu, S., 1984. Fed-batch techniques in microbial processes. In: Fiechter, A. (Ed.), Advance in Biochemical Engineering/Biotechnology, vol. 30, Springer-Verlag, New York, pp. 145-194.