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An adaptive control algorithm for fed-batch culture.

J Stanissis1, D Levisauskas

  • 1Kaunas Polytechnical Institute, Lithuanian S.S.R., Soviet Union.

Biotechnology and Bioengineering
|May 1, 1984
PubMed
Summary

This study introduces adaptive control algorithms for fed-batch cultures to maximize product yield using a self-adjusting model. Simulation results demonstrate the effectiveness of these algorithms for real-time bioprocess optimization.

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Area of Science:

  • Biotechnology and Biochemical Engineering
  • Process Control and Automation

Background:

  • Fed-batch cultures are crucial for biopharmaceutical production.
  • Maximizing product output requires precise control of fermentation parameters.
  • Existing control methods may lack adaptability to dynamic biological systems.

Purpose of the Study:

  • To develop and evaluate adaptive control algorithms for fed-batch cultures.
  • To maximize product yield through a self-adjusting model.
  • To enable on-line process control for fermentation.

Main Methods:

  • Applied optimization methods to a generalized mathematical model of fed-batch fermentation.
  • Developed adaptive control algorithms for on-line process control.
  • Simulated an amylolytic enzyme fermentation model to test algorithm efficiency.
  • Introduced parameter time variation limitations to improve identification convergence.

Main Results:

  • Demonstrated the efficiency of proposed adaptive algorithms through simulations.
  • Identified on-line parameter identification as a key challenge in adaptive control.
  • Showcased improved convergence of the identification algorithm using parameter limitations.

Conclusions:

  • The developed adaptive control algorithms are effective for fed-batch cultures.
  • Parameter variation constraints enhance the reliability of on-line model identification.
  • Further investigation on real-world bioprocesses is recommended.