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Optimal dynamic experiments for bioreactor model discrimination

M J Cooney1, K A McDonald

  • 1Department of Chemical Engineering and Materials Science, University of California, Davis 95616, USA.

Applied Microbiology and Biotechnology
|October 1, 1995
PubMed
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This study introduces a dynamic model discrimination method for microbial continuous cultures. The approach effectively identified accurate models for E. coli and C. utilis, improving bioprocess understanding.

Area of Science:

  • Biotechnology
  • Microbial Physiology
  • Process Engineering

Background:

  • Accurate dynamic models are crucial for optimizing microbial continuous cultures.
  • Distinguishing between various model structures is challenging but essential for reliable predictions.
  • Previous methods often lack robust strategies for selecting optimal experimental inputs.

Purpose of the Study:

  • To develop and present a general approach for dynamic model discrimination in continuous cultures.
  • To obtain and validate dynamic models for pure cultures of E. coli and C. utilis.
  • To compare the effectiveness of different discrimination functions in selecting optimal experimental inputs.

Main Methods:

  • Four candidate models with varying complexity were considered for each microbial system.

Related Experiment Videos

  • Multivariable step inputs were optimized and applied to discriminate between models.
  • Model predictions were compared against experimental data to select the best-fitting models.
  • Two discrimination functions were evaluated for their ability to optimize input selection.

    • Main Results:

    • The dynamic model discrimination approach successfully identified distinct models for E. coli and C. utilis.
    • Model discrimination based on maximizing the minimum absolute difference between models showed high potential.
    • Selected models accurately predicted the dynamic behavior of the pure cultures.
    • The optimized input strategy effectively differentiated between candidate models.

    Conclusions:

    • The proposed dynamic model discrimination method is effective for continuous cultures.
    • The validated models provide improved representations of E. coli and C. utilis dynamics.
    • This approach enhances the reliability of microbial process modeling and optimization.