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This study introduces automated methods for creating process models from fermentation data. It enhances model reliability by analyzing noisy measurements and detecting biological phenomena, improving process understanding.

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Area of Science:

  • Biotechnology and biochemical engineering
  • Process systems engineering
  • Computational biology

Background:

  • Fermentation processes generate complex, noisy data.
  • Accurate process models are crucial for optimization and control.
  • Existing methods struggle with inherent data variability and sampling effects.

Purpose of the Study:

  • To develop automated methods for proposing structured process models from fed-batch fermentation data.
  • To enhance the reliability of biological phenomena detection from noisy measurements.
  • To validate the proposed modeling approach using real experimental data.

Main Methods:

  • Numerical compensation of measurements for feeding and sampling influences.
  • Probabilistic framework for dividing compensated curves into episodes.
  • Calculation of probabilities for biological phenomena detection.
  • Uncertainty analysis of phenomena detection based on measurement influences.
  • Automatic proposal of model structures based on detected phenomena.

Main Results:

  • Demonstrated a method to automatically generate structured process models.
  • Successfully handled noisy fermentation data using a probabilistic episodic approach.
  • Quantified the uncertainty in biological phenomena detection.
  • Experimental validation using Streptomyces tendae fed-batch cultivation data.

Conclusions:

  • The developed methods enable automated process model proposal from fermentation data.
  • The approach improves the handling of noisy and complex experimental data.
  • Reliable detection of biological phenomena is achievable, aiding in model development.