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.
Highlights
As S. cerevisiae investigations continue in research and development of food and drug need improvement, as well as biotechnological and genetic purposes
Hapoglu et al (2001) utilized a Controlled Auto Regressive Integrated Moving Average (CARIMA) model and its parameters were identified with Bierman computation procedure in which data obtained by enforcing the system with a pseudo random binary sequence (PRBS)
The model testing was achieved by using the integral of absolute error (IAE) and the integral square of error (ISE) criteria and parameter estimation error norm (PEEN) criteria which is given in Eq 13
Summary
As S. cerevisiae investigations continue in research and development of food and drug need improvement, as well as biotechnological and genetic purposes. Svoronos et al, (1981) reported a bilinear model based upon minimum-variance self-tuning rule Their model effectiveness was examined by using the simulations. Hapoglu et al (2001) utilized a Controlled Auto Regressive Integrated Moving Average (CARIMA) model and its parameters were identified with Bierman computation procedure in which data obtained by enforcing the system with a pseudo random binary sequence (PRBS). Akay et al (2003) investigated parametric and non-parametric models which include the relationship between dissolved oxygen concentration and air flow rate in S. cerevisiae production medium. These models theoretical and experimental identification were realized. The models of Baker’s yeast production in a batch process are obtained experimentally and theoretically.
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