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Extended Kalman filter (EKF) application in vitamin C two-step fermentation process

D Wei1, W Yuan, Z Yuan

  • 1East China University of Chemical Technology, Shanghai.

Chinese Journal of Biotechnology
|January 1, 1993
PubMed
Summary

The extended Kalman filter (EKF) accurately estimates vitamin C fermentation parameters, improving model predictions. This kinetic model study enhances understanding of fermentation processes and provides a basis for state estimation.

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Area of Science:

  • Biochemical Engineering
  • Process Systems Engineering
  • Fermentation Technology

Background:

  • Vitamin C fermentation involves complex kinetics.
  • Fermentation processes are susceptible to noise from various sources.
  • Accurate kinetic modeling is crucial for process optimization.

Purpose of the Study:

  • To apply extended Kalman filter (EKF) theory to a vitamin C two-step fermentation kinetic model.
  • To improve the accuracy of process parameter estimation in the presence of white noise.
  • To provide a basis for state estimation and prediction in fermentation systems.

Main Methods:

  • Kinetic model study of vitamin C two-step fermentation.
  • Application of extended Kalman filter (EKF) theory.

Related Experiment Videos

  • Analysis of process parameters affected by model, system, and operational fluctuations.
  • Comparison of EKF-estimated parameters with experimental results.

    • Main Results:

    • EKF significantly improved the agreement between calculated and experimental results compared to predictions without parameter estimation.
    • Estimated process parameters provided a better understanding of fermentation kinetics.
    • The study demonstrated the effectiveness of EKF in handling noise-corrupted fermentation data.

    Conclusions:

    • Extended Kalman filter (EKF) is a robust method for parameter estimation in vitamin C fermentation.
    • Accurate parameter estimation enhances the reliability of kinetic models.
    • The findings support the use of EKF for improved state estimation and prediction in bioprocesses.