Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Optimization of feeding profile for a fed-batch bioreactor by an evolutionary algorithm.

M Ronen1, Y Shabtai, H Guterman

  • 1Unit of Biotechnology, Faculty of Engineering Sciences, Ben-Gurion University of the Negev, P.O. Box 653, Beer-Sheva 84105, Israel.

Journal of Biotechnology
|June 27, 2002
PubMed
Summary

An evolutionary algorithm optimized fed-batch processes by designing feeding profiles. This approach improved process performance and addressed model mismatches through online optimization, enhancing efficiency.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Smart respiratory monitoring: clinical development and validation of the IPI™ (Integrated Pulmonary Index) algorithm.

Journal of clinical monitoring and computing·2016
Same author

Neural-network classifiers for automatic real-world aerial image recognition.

Applied optics·2010
Same author

Multiple levels of gene regulation mediate differentiation of the intracellular pathogen Leishmania.

FASEB journal : official publication of the Federation of American Societies for Experimental Biology·2010
Same author

Unsupervised speaker recognition based on competition between self-organizing maps.

IEEE transactions on neural networks·2008
Same author

Distinction of cervical cancer biopsies by use of infrared microspectroscopy and probabilistic neural networks.

Applied optics·2005
Same author

Possible common biomarkers from FTIR microspectroscopy of cervical cancer and melanoma.

Journal of microscopy·2004

Area of Science:

  • Biochemical Engineering
  • Process Optimization
  • Computational Biology

Background:

  • Fed-batch processes are crucial in biotechnology and chemical manufacturing.
  • Designing optimal feeding profiles is essential for maximizing product yield and process efficiency.
  • Traditional methods often struggle with model uncertainties and dynamic process changes.

Purpose of the Study:

  • To develop and evaluate an evolutionary algorithm for designing optimal feeding profiles in fed-batch processes.
  • To investigate the effectiveness of an online optimization scheme in overcoming model mismatches.
  • To demonstrate improved process performance using the optimized feeding strategy.

Main Methods:

  • Utilized an evolutionary algorithm with chromosomes representing feeding profile parameters (feed rates, arc parameters, switching times).

Related Experiment Videos

  • Tested the algorithm on fed-batch process simulations, comparing roulette wheel and geometric ranking selection functions.
  • Implemented a novel online optimization scheme involving process sampling and model updating.
  • Validated the approach on experimental fed-batch processes using complex, non-differentiable models.

    • Main Results:

    • The evolutionary algorithm successfully designed smooth and analytically comparable feeding profiles.
    • Online optimization demonstrated significant improvements in the objective function, especially at lower sampling frequencies.
    • The optimized feeding profile led to enhanced overall process performance in experimental studies.
    • The method proved effective even with complex, non-differentiable process models.

    Conclusions:

    • Evolutionary algorithms provide a robust framework for optimizing fed-batch feeding profiles.
    • Online model updating and optimization effectively mitigate challenges posed by model mismatches.
    • The developed strategy enhances process performance and efficiency in real-world fed-batch operations.