Modelling of Baker’s Yeast Production

Abstract

In the present work, parametric models for the control of bioreactor temperature have been applied. Various order discrete time model parameters were evaluated theoretically and experimentally. Two types of input signals were used as external force to determine Auto Regressive Moving Average with Exogenous (ARMAX) model parameters with Recursive Least Square (RLS) parameter estimation algorithm. The third order ARMAX model is utilized, and compared with the second order one. Ternary and square disturbances are given to the cooling water flow rate which can be chosen as manipulating variable in closed loop cases. System response is monitored continuously and the model parameters are calculated. The models with experimentally identified parameters are compared with ones that their parameters are identified theoretically.

Keywords

• Baker’s Yeast
• System Identification
• Saccharomyces cerevisiae as a second metabolite source

Modelling of Baker’s Yeast Production

Abstract

In the present work, parametric models for the control of bioreactor temperature have been applied. Various order discrete time model parameters were evaluated theoretically and experimentally. Two types of input signals were used as external force to determine Auto Regressive Moving Average with Exogenous (ARMAX) model parameters with Recursive Least Square (RLS) parameter estimation algorithm. The third order ARMAX model is utilized, and compared with the second order one. Ternary and square disturbances are given to the cooling water flow rate which can be chosen as manipulating variable in closed loop cases. System response is monitored continuously and the model parameters are calculated. The models with experimentally identified parameters are compared with ones that their parameters are identified theoretically.

Keywords

• Baker’s Yeast
• System Identification
• Saccharomyces cerevisiae as a second metabolite source

References

1. Akay, B., Ertunc, S., Boyacioglu H., Hapoglu, H., Alpbaz, M. (2011). Discrete system identification and self-tuning control of dissolved oxygen concentration in a stirred reactor. Korean J of Chem. Eng., 28(3), 837-847.
2. Huang, B., Guo J., Yi, B., Yu, X., Sun, L., Chen, W. (2008). Heterologous production of secondary metabolites as pharmaceuticals in Saccharomyces cerevisiae. Biotechnol Lett., 30, 1121-1137.
3. Ertunc, S., Akay B., Bursali N., Hapoglu H., Alpbaz M. (2003). Generalized minimum variance control of growth medium temperature of Baker’s yeast production, Food and Bioproducts Processing, 81, 327-335.
4. Warwick, K., Rees, D. (1988). Industrial Digital Control Systems. Peter Peregrinus, London.
5. Svoronos, S., Stephanopoulos, G., Aris, R. (1981). On bilinear estimation and control, Int J Control, 34(4), 651-684.
6. Hapoglu, H., Karacan, S., Koca Erten, Z.S., Alpbaz, M. (2001). Parametric and nonparametric model based control of a packed distillation column. Chem. Eng. and Processing, 40, 537-544.
7. Akay, B., Ertunc ,S., Boyacioglu, H., Hapoglu, H., Alpbaz, M. (2003). Parametric and non-parametric models’ identification based-on dissolved oxygen concentration in S.cerevisiae production. 3*nd Chemical Engineering Conference for Collaborative Research in Eastern Mediterranean (EMCC-3), Thesaloniki (Greece)
8. Zhu, X., Seborg, D.E. (1994). Non-linear predictive control based on Hammerstein models. PSE’94 Conference, June, Kyongju, Korea.

9. Norquay, S., Palazoglu, A., Romagnoli, J.A. (1996). Nonlinear model predictive control of pH neutralization using Wiener models. Proceedings of the IFAC World Conference, San Francisco, M, 31.
10. Wang, D., Zhou, D.H., Jin, Y.H., Qin, S.J. (2004). Adaptive generic model control for a class of non-linear time varying processes with input time delay. J. Process Contr., 14, 517-531.
11. Sotomayor, O.A.Z., Park, S.W. (2003). Garcia C. Multivariable identification of an activated sludge process with subspace based algorithms. Contr. Eng. Pract., 11: 961- 969.
12. Sima, V., Sima, D.M., Van Huffel, S. (2004). High performance numerical algorithms and software for subspace-based linear multivariable system identification. J. Com. Appl. Math., 170, 371-397.
13. Soderström, T., Stoica, P. (1998). System Identification. Prentice Hall Ltd. New York.
14. Boyacıoğlu, H. (2013). Çok Girdili-Çok Çıktılı Kontrolun Ekmek Mayası Üretimine Uygulanması. Doktora Tezi, Ankara Üniversitesi Fen Bilimleri Enstitüsü, Ankara.