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Mathematical Problems in Engineering
Volume 2012, Article ID 789156, 21 pages
http://dx.doi.org/10.1155/2012/789156
Research Article

Distribution-Based Identification of Yield Coefficients in a Baker’s Yeast Bioprocess

Department of Automatic Control, University of Craiova, A.I. Cuza 13, 200585 Craiova, Romania

Received 24 August 2011; Revised 16 November 2011; Accepted 30 November 2011

Academic Editor: Alexander Pogromsky

Copyright © 2012 Dorin Sendrescu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. G. Bastin and D. Dochain, On-Line Estimation and Adaptive Control of Bioreactors, Elsevier, New York, NY, USA, 1990.
  2. M. Roman and D. Selişteanu, “Modeling issues and structural properties of a class of nonlinear bioprocesses,” International Review of Automatic Control, vol. 3, no. 6, pp. 578–587, 2010, ISSN 1974-6059. View at Google Scholar
  3. M. Henson, “Dynamic modelling and control of yeast cell populations in continuous biochemical reactors,” Computers & Chemical Engineering, vol. 27, no. 8-9, pp. 1185–1199, 2003. View at Google Scholar
  4. F. Renard and A. Vande Wouwer, “Robust adaptive control of yeast fed-batch cultures,” Computers & Chemical Engineering, vol. 32, no. 6, pp. 1238–1248, 2008. View at Google Scholar
  5. D. Selişteanu, E. Petre, and V. B. Răsvan, “Sliding mode and adaptive sliding-mode control of a class of nonlinear bioprocesses,” International Journal of Adaptive Control and Signal Processing, vol. 21, no. 8-9, pp. 795–822, 2007. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  6. H. Unbehauen and G. P. Rao, Identification of Continuous Systems, vol. 10, North-Holland, Amsterdam, The Netherlands, 1987.
  7. L. M. Li and S. A. Billings, “Continuous time non-linear system identification in the frequency domain,” International Journal of Control, vol. 74, no. 11, pp. 1052–1061, 2001. View at Publisher · View at Google Scholar · View at MathSciNet
  8. I. D. Landau, B. D. O. Anderson, and F. De Bruyne, “Recursive identification algorithms for continuous-time nonlinear plants operating in closed loop,” Automatica, vol. 37, no. 3, pp. 469–475, 2001. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  9. B. R. J. Haverkamp, C. T. Chou, M. Verhaegen, and R. Johansson, “Identification of continuous-time MIMO state space models from sampled data, in the presence of process and measurement noise,” in Proceedings of the 35th IEEE Conference on Decision and Control, pp. 1539–1544, December 1996. View at Scopus
  10. P. Overschee and B. De Moor, Subspace System Identification for Linear Systems, Kluwer Academic, Boston, Mass, USA, 1996.
  11. C. Marin, “System identification based on distribution theory,” in Proceedings of the IASTED International Conference on Applied Simulation and Modelling (ASM '02), pp. 456–462, Crete, Greece, 2002.
  12. A. Ohsumi, K. Kameyama, and K.-I. Yamaguchi, “Subspace identification for continuous-time stochastic systems via distribution-based approach,” Automatica, vol. 38, no. 1, pp. 63–79, 2002. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  13. A. E. Pearson and F. C. Lee, “On the identification of polynomial input-output differential systems,” IEEE Transactions on Automatic Control, vol. 30, no. 8, pp. 778–782, 1985. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  14. A. Patra and H. Unbehauen, “Identification of a class of nonlinear continuous-time systems using Hartley modulating functions,” International Journal of Control, vol. 62, no. 6, pp. 1431–1451, 1995. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  15. D. Selisteanu, E. Petre, and D. Şendrescu, “Modeling and identification of wastewater biodegradation process,” in Proceedings of the 12th International Symposium on Modeling, Simulation and System's Identification (SIMSIS '04), pp. 149–154, Galati, Romania, 2004.
  16. D. Beluhan, D. Gosak, N. Pavlovic, and M. Vampola, “Biomass estimation and optimal control of the baker's yeast fermentation process,” Computers & Chemical Engineering, vol. 19, supplement 1, pp. 387–392, 1995. View at Google Scholar
  17. L. Chen, Modelling, identifiability and control of complex biotechnological systems, Ph.D. thesis, Université Catholique de Louvain, Belgium, 1992.
  18. C. Rocha and E. C. Ferreira, “Design of optimal experiments for identification of yield coefficients in a baker’s yeast model,” in Proceedings of the 1st European Symposium on Biochemical Engineering Science, B. Glennon, P. M. Kieran, and K. Ch. A. M. Luyben, Eds., pp. 99–100, Dublin, Ireland, 1996.
  19. C. Pertev, M. Türker, and R. Berber, “Dynamic modeling, sensitivity analysis and parameter estimation of industrial yeast fermenters,” Computers and Chemical Engineering, vol. 21, no. 1, pp. S739–S744, 1997. View at Google Scholar · View at Scopus
  20. B. Sonnleitner and O. Kappeli, “Growth of Saccharomyces cerevisiae is controlled by its respiratory capacity: formulation and verification of a hypothesis,” Biotechnology and Bioengineering, vol. 28, no. 6, pp. 927–937, 1986. View at Google Scholar · View at Scopus
  21. S. Feyo De Azevedo, B. Dahm, and F. R. Oliveira, “Hybrid modelling of biochemical processes: a comparison with the conventional approach,” Computers and Chemical Engineering, vol. 21, no. 1, pp. S751–S756, 1997. View at Google Scholar · View at Scopus
  22. R. Oliveira, E. C. Ferreira, F. Oliveira, and S. Feyo De Azevedo, “A study on the convergence of observer-based kinetic estimators in fed-batch fermentations,” Journal of Process Control, vol. 6, no. 6, pp. 367–371, 1996. View at Publisher · View at Google Scholar · View at Scopus
  23. Y. Pomerleau and M. Perrier, “Estimation of multiple specific growth rates in bioprocesses,” AIChE Journal, vol. 36, no. 2, pp. 207–215, 1990. View at Google Scholar · View at Scopus
  24. L. Schwartz, Théorie des Distributions, Paris, France, 1951.
  25. P. Bogaerts, “A hybrid asymptotic-Kalman observer for bioprocesses,” Bioprocess and Biosystems Engineering, vol. 20, no. 3, pp. 249–255, 1999. View at Google Scholar
  26. M. N. Karim and S. L. Rivera, “Artificial neural networks in bioprocess state estimation,” in Advances in Biochemical Engineering/Biotechnology, A. Fiechter, Ed., vol. 46 of Modern Biochemical Engineering, pp. 1–34, Springer, Berlin, Germany, 1992. View at Google Scholar
  27. P. Ascencio, D. Sbarbaro, and S. F. de Azevedo, “An adaptive fuzzy hybrid state observer for bioprocesses,” IEEE Transactions on Fuzzy Systems, vol. 12, no. 5, pp. 641–651, 2004. View at Publisher · View at Google Scholar · View at Scopus
  28. D. Sendrescu, M. Roman, C. Marin, E. Bobasu, and D. Selisteanu, “Off-line identification of yield coefficients in a Baker’s yeast bioprocess,” in Proceedings of the 29th Chinese Control Conference, pp. 1254–1260, IEEE catalog no. CFP1040A-CDR, ISBN 978-7-89463-104-6, Beijing, China, July 2010.