Modelling of Baker’s Yeast Production

Key Findings & Data:Ternary and square disturbances are given to the cooling water flow rate which can be chosen as manipulating variable in closed loop cases.

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Conclusion & Impact:The models with experimentally identified parameters are compared with ones that their parameters are identified theoretically.

🔬Design:Modeling & Simulation

📍Scope:Comprehensive Sample

⚙️Variables:Baker’s Yeast · System Identification · Saccharomyces cerevisiae as a second metabolite source

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.

Özet

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

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Cite This Article

HAPOĞLU, H., ERTUNÇ, S., BOYACIOĞLU, H. (2022). Modelling of Baker’s Yeast Production. International Journal of Secondary Metabolite, Cilt: 4 - Sayı: 1, 10–17. https://ajindex.com/articles/541763

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