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Fuzzy control of bioprocess
1Department of Biotechnology, Graduate School of Engineering, Nagoya University, Chikusa-ku, Nagoya 464-8603, Japan.
Journal of Bioscience and Bioengineering
|October 20, 2005
Summary
Expert bioprocess control relies on linguistic rules. Fuzzy control, using fuzzy inference, translates these rules into computational algorithms for industrial applications like vitamin B2 production.
Area of Science:
- Biotechnology
- Control Engineering
Background:
- Bioprocesses traditionally rely on expert judgment and experience, often formalized as linguistic IF-THEN rules.
- Fuzzy inference offers a computational method to integrate these linguistic rules into process control algorithms.
Purpose of the Study:
- To review the industrial applications of fuzzy control in bioprocesses.
- To highlight how fuzzy control translates expert knowledge into automated operational strategies.
Main Methods:
- Utilizing fuzzy inference to process linguistic rules for bioprocess control.
- Categorizing fuzzy control into direct (process variables) and indirect (phase recognition) approaches.
- Examining case studies of industrial fuzzy control implementation.
Main Results:
- Fuzzy control effectively incorporates expert knowledge into bioprocess operations.
- Direct fuzzy control manages variables like feed rate and temperature.
- Indirect fuzzy control uses phase recognition for complex bioprocesses.
Conclusions:
- Fuzzy control is a viable and practical tool for optimizing industrial bioprocesses.
- Successful industrial implementations include pravastatin precursor, vitamin B2, and sake production.
- This review showcases the successful integration of fuzzy logic in real-world bioprocess control.