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Light-Controlled Fermentations for Microbial Chemical and Protein Production
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Published on: March 22, 2022

Physiological state control of fermentation processes.

K Konstantinov1, T Yoshida

  • 1International Center of Cooperative Research in Biotechnology, Department of Fermentation Technology, Faculty of Engineering, Osaka University, Yamada-kami, Suita-shi, Osaka 565, Japan.

Biotechnology and Bioengineering
|April 5, 1989
PubMed
Summary
This summary is machine-generated.

A novel physiological state control approach for fermentation processes is introduced, eliminating the need for conventional models. This method uses artificial intelligence to adapt control strategies based on the cell culture's real-time physiological state.

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Area of Science:

  • Biotechnology
  • Process Control
  • Fermentation Engineering

Background:

  • Fermentation processes exhibit variable cell population characteristics, termed "physiological state."
  • Dynamic changes in physiological state necessitate flexible control strategies.
  • Conventional mathematical models are often insufficient for real-time fermentation control.

Purpose of the Study:

  • Introduce a novel "physiological state control approach" for fermentation processes.
  • Develop a control system that represents processes via the cell culture's current physiological state.
  • Eliminate the requirement for conventional mathematical models in control system synthesis.

Main Methods:

  • Decompose the physiological state space into "physiological situations."
  • Apply invariant control strategies within each stable physiological situation.
  • Develop an online algorithm for recognizing the current physiological state using AI, fuzzy sets, and pattern recognition theory.

Main Results:

  • Demonstrated practical realization using data from a continuous single cell protein production fermentation.
  • Successfully implemented an online recognition algorithm for dynamic physiological states.
  • Showcased the effectiveness of adaptive control strategies based on identified physiological situations.

Conclusions:

  • The physiological state control approach offers a flexible and effective alternative to conventional methods.
  • AI-based methods, particularly fuzzy sets and pattern recognition, are crucial for real-time physiological state recognition.
  • This approach enhances the control and adaptability of fermentation processes, as shown in single cell protein production.