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Precise, High-throughput Analysis of Bacterial Growth
Published on: September 19, 2017
An algorithmic approach to constructing the on-line estimation system for the specific growth rate
H Shimizu1, T Takamatsu, S Shioya
1Department of Chemical Engineering, Kyoto University, Kyoto 606, Japan.
Biotechnology and Bioengineering
|January 15, 1989
Summary
This study introduces an algorithm for accurately estimating specific growth rates during fermentation. The method utilizes macroscopic balance and an extended Kalman filter, improving upon existing techniques for real-time process monitoring.
Area of Science:
- Biotechnology
- Chemical Engineering
- Process Control
Background:
- Online estimation of specific growth rate is crucial for optimizing fermentation processes.
- Existing methods often lack guidance on selecting observed variables and tuning Extended Kalman Filter (EKF) parameters.
- Accurate real-time monitoring of microbial growth is essential for process control and yield maximization.
Purpose of the Study:
- To propose a novel algorithm for the on-line estimation of specific growth rate in batch and fed-batch fermentation.
- To provide practical guidelines for selecting observed variables and tuning EKF parameters.
- To enhance the accuracy and reliability of real-time fermentation process monitoring.
Main Methods:
- Utilizing macroscopic balance and the Extended Kalman Filter (EKF) for real-time estimation.
- Employing the condition number as a criterion for selecting optimal observed variables.
- Developing criteria for validating estimated specific growth rates and adjusting EKF parameters (initial state values, covariance matrices).
Main Results:
- The condition number is identified as a practical and valid criterion for observed variable selection.
- Proposed criteria enable effective tuning of EKF parameters for accurate specific growth rate estimation.
- The developed algorithm accurately estimates specific growth rates in batch and fed-batch fermentations.
- Constant-element covariance matrix EKF outperformed adaptive EKF when cell concentration was directly measured.
Conclusions:
- The proposed algorithm offers a robust and accurate method for on-line specific growth rate estimation in fermentation.
- Practical guidance on variable selection and EKF tuning enhances the applicability of real-time estimation techniques.
- This approach provides valuable insights for process optimization and control in biotechnological applications.
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