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Estimation for model parameters of batch fermentation kinetics.

B Fang1, J Lin

  • 1Department of Chemical and Biochemical Engineering, Hua Qiao University, Quanzhou, China.

Chinese Journal of Biotechnology
|January 1, 1992
PubMed
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This study presents an analytical solution for fermentation kinetics, accurately estimating key parameters like maximum growth rate (mumax) and substrate affinity (Ks) using the POWELL algorithm. Results confirm the model

Area of Science:

  • Biochemical Engineering
  • Biotechnology
  • Microbial Fermentation

Background:

  • Fermentation kinetics modeling is crucial for optimizing bioprocesses.
  • Accurate estimation of kinetic parameters (mumax, Ks, beta, YG, YP, m) is essential for predictive modeling.
  • Batch fermentation processes require specific kinetic descriptions.

Purpose of the Study:

  • To deduce an analytical solution for fermentation kinetics based on established mathematical models.
  • To estimate key fermentation kinetic parameters simultaneously using an optimization algorithm.
  • To validate the analytical solution against experimental data from a lysine fermentation process.

Main Methods:

  • Deduction of an analytical solution for batch fermentation kinetics.

Related Experiment Videos

  • Simultaneous estimation of kinetic parameters (mumax, Ks, beta, YG, YP, m) using the POWELL optimization algorithm.
  • Utilizing experimental data from a Corynebacterium glutamicum batch lysine fermentation.

    • Main Results:

    • The derived analytical solution demonstrated excellent agreement with experimental data.
    • The POWELL optimization algorithm effectively estimated all kinetic parameters in a single step.
    • Lysine synthesis rate was found to be dependent on both biomass growth rate and concentration.

    Conclusions:

    • The analytical solution provides a robust method for describing batch fermentation.
    • Simultaneous parameter estimation using the POWELL algorithm is efficient and accurate.
    • Understanding the relationship between growth rate, biomass concentration, and product synthesis is key for optimizing lysine production.