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Published on: June 28, 2018
On-line state recognition in a yeast fed-batch culture using error vectors.
1Department of Biotechnology, Faculty of Engineering, Osaka University, Suita, Osaka 565, Japan.
Biotechnology and Bioengineering
|July 20, 1995
Summary
This study introduces a fuzzy logic system to monitor yeast fed-batch cultures. The system accurately identifies physiological states, including unusual aerobic ethanol production with low cell growth, enabling better process control.
Area of Science:
- Biotechnology
- Biochemical Engineering
- Yeast Fermentation
Background:
- Monitoring physiological states in yeast fed-batch cultures is crucial for optimizing ethanol production and cell growth.
- Traditional methods may lack the precision to identify subtle or unusual physiological shifts in real-time.
Purpose of the Study:
- To develop an on-line system for recognizing physiological states in yeast fed-batch cultures using fuzzy inferencing.
- To accurately characterize cell growth and ethanol production dynamics.
Main Methods:
- A novel error vector was defined based on macroscopic elemental balance equations.
- Fuzzy membership functions were created from experimental error vector distributions.
- Fuzzy inferencing was employed for real-time physiological state recognition.
Main Results:
- The system accurately identified various physiological states, including a specific condition of aerobic ethanol production with minimal cell growth.
- The P/O ratio (energy parameter) was adaptively estimated post-state recognition.
- Cell growth was successfully evaluated using the estimated P/O ratio.
Conclusions:
- Fuzzy inferencing provides an effective method for on-line recognition of physiological states in yeast fed-batch cultures.
- This approach allows for accurate monitoring and adaptive control, improving process understanding and optimization.
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