On-line identification of the state and parameters for fed-batch penicillin fermentation process

Area of Science:

• Biochemical Engineering
• Process Control
• Mathematical Modeling

Background:

• Penicillin fed-batch fermentation requires precise monitoring for optimal yield.
• Real-time identification of fermentation states and parameters is crucial for adaptive control.
• Existing models may not adequately account for process noise in industrial settings.

Purpose of the Study:

• To develop and validate a mathematical model for penicillin fed-batch fermentation incorporating noise.
• To implement an extended Kalman filter (EKF) for real-time state and parameter estimation.
• To provide a foundation for adaptive optimal control strategies in fermentation processes.

Main Methods:

• Development of a mathematical model for penicillin fed-batch fermentation.
• Utilization of carbon dioxide production rate as an online measured variable.
• Application of the extended Kalman filter (EKF) for state and parameter identification.

Main Results:

• The extended Kalman filter (EKF) successfully identified states and parameters in real-time.
• Estimated state values from the EKF closely matched experimental data.
• The EKF demonstrated superior performance as an online observer for fermentation dynamics.

Conclusions:

• The developed mathematical model and EKF provide a robust framework for monitoring penicillin fermentation.
• The EKF is an effective tool for real-time observation of fermentation states and dynamic parameters.
• This approach supports the development of adaptive optimal control for enhanced fermentation efficiency.