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Saccharomyces cerevisiae Exponential Growth Kinetics in Batch Culture to Analyze Respiratory and Fermentative Metabolism
Published on: September 30, 2018
Profile control scheme in a Bakers' yeast fed-batch culture
T Takamatsu1, S Shioya, Y Okada
1Department of Chemical Engineering, Kyoto University, Kyoto, 606 Japan.
Biotechnology and Bioengineering
|December 1, 1985
Summary
This study introduces a computer control system for precise biomass and growth rate tracking. The programmed-controller/feedback-compensator (PF) system, enhanced with model reference adaptive control (MRAC), effectively manages microbial growth profiles.
Area of Science:
- Biotechnology
- Control Systems Engineering
- Bioprocess Engineering
Background:
- Accurate control of biomass concentration and specific growth rate is crucial for optimizing bioprocesses.
- Existing control strategies may struggle with noise and model parameter uncertainties.
Purpose of the Study:
- To develop and validate a practical computer control scheme for precise profile following of biomass concentration and specific growth rate.
- To introduce a novel control system integrating programmed control with adaptive feedback compensation.
Main Methods:
- Development of the programmed-controller/feedback-compensator (PF) system.
- Integration of the model reference adaptive control (MRAC) algorithm as a feedback compensator.
- Addition of a predictive control algorithm for cell concentration control, forming the PFP system.
Main Results:
- Computer simulations confirmed the validity and effectiveness of the proposed PF system.
- The PF system combined with MRAC (PF-MRAC) demonstrated sufficient performance for specific growth rate profile control.
- The PFP system, incorporating predictive control and MRAC (PFP-MRAC), showed excellent performance for cell concentration profile control.
Conclusions:
- The proposed PF and PFP systems, particularly with MRAC, offer robust and accurate solutions for bioprocess profile control.
- These advanced control schemes effectively handle disturbances and model uncertainties, enhancing bioprocess predictability and optimization.
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