Abstract
The performance of a continuous-time Recursive Least Squares (CRLS) and a discrete-time Recursive Least Squares (DRLS) algorithms are examined for the growth medium temperature control of a cooling batch bioreactor in which Saccharomyces cerevisiae growth at aerobic condition by using Continuous-time Generalised Predictive Control (CGPC) algorithm. MATLAB programme was utilized for recursive parameter identification algorithms (CRLS and DRLS). The success or otherwise of these algorithms are estimated using parameter norm criterion for the various order of models and several input signals. There is a considerable improvement of identification algorithms with the reduced order of models. It has been shown that the performance of a DRLS algorithm is as successful as the other recursive parameter identification of a continuous-time system model.
Chapter PDF
Similar content being viewed by others
Keywords
- Generalize Minimum Variance
- Minimum Variance Control
- Bioproducts Processing
- Time Transfer Function
- Model Pole Zero
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Shuler, M.L., Kargi, F.: Bioprocess Engineering. Prentice Hall, New Jersey (1992)
Bursali, N., Akay, B., Ertunc, S., Hapoglu, H., Alpbaz, M.: New Tuning Method for Generalized Predictive Control of the Production of S.cerevisiae. Food and Bioproducts Processing 79, 27–34 (2001)
Bailey, J.E., Ollis, D.F.: Biochem. Eng. Fund., 2nd edn. Mc Graw Hill, New York (1986)
Cinar, A., Parulekar, S.J., Undey, C., Birol, G.: Fermentation Modeling Monitoring and Control. Marcel Dekker Inc., New York (2003)
Ertunc, S., Akay, B., Bursali, N., Hapoglu, H., Alpbaz, M.: Generalized Minimum Variance Control of Growth Medium Temperature of Baker’s Yeast Production. Food and Bioproducts Processing 81, 327–335 (2003)
Warwick, K., Rees, D.: Industrial Digital Control Systems. Peter Peregrinus, London (1988)
Sinha, N.K.: Identification of Continuous-time Systems from Samples of Input-Output data: An Introduction. Sadhana 25(part 2), 75–83 (2000)
Wang, L., Gawthrop, P.: On the Estimation of Continuous Time Transfer Functions. International Journal of Control 74, 889–904 (2001)
Kowalczuk, Z., Kozlowski, J.: Continuous-time Approaches to Identification of Continuous-time Systems. Automatica 36, 1229–1236 (2000)
Subrahmanyam, A.V.B., Saha, D.C., Rao, G.P.: Irreducible Continuous Model Identification via Markov Parameter Estimation. Automatica 32, 249–253 (1996)
Demircioglu, H.: Continuous-time Generalised Predictive Control (CGPC): Implementation Issues. In: Conference on Advances in Model-Based Predictive Control, UK, pp. 145–159 (1993)
Gawthrop, P.J.: Continuous-time Self-Tuning Control, vol. 1-Design. Research Studies Press, Letchworth (1987)
Wellstead, P.E., Zarrop, M.B.: Self-tuning Systems-Control and Signal Processing. John Wiley & Sons, New York (1991)
Rao, M., Qui, H.: Process Control Engineering. Gordon and Breach Science Publishers, Switzerland (1993)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ertunc, S., Akay, B., Hapoglu, H., Alpbaz, M. (2005). A New Computer Algorithm Approach to Identification of Continuous-Time Batch Bioreactor Model Parameters. In: Sunderam, V.S., van Albada, G.D., Sloot, P.M.A., Dongarra, J.J. (eds) Computational Science – ICCS 2005. ICCS 2005. Lecture Notes in Computer Science, vol 3514. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11428831_19
Download citation
DOI: https://doi.org/10.1007/11428831_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26032-5
Online ISBN: 978-3-540-32111-8
eBook Packages: Computer ScienceComputer Science (R0)