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Related Experiment Videos

Multi-objective optimization in Aspergillus niger fermentation for selective product enhancement.

Chaitali Mandal1, Ravindra D Gudi, G K Suraishkumar

  • 1Department of Chemical Engineering, IIT Bombay, 400 076 Powai, Mumbai, India.

Bioprocess and Biosystems Engineering
|October 12, 2005
PubMed
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This study introduces a multi-objective optimization method for fermentation processes, enhancing specific enzyme production like catalase and protease in Aspergillus niger. The novel approach significantly boosts enzyme yields compared to traditional methods.

Area of Science:

  • Biotechnology
  • Biochemical Engineering
  • Process Optimization

Background:

  • Multi-product fermentation presents challenges in optimizing yields for specific products.
  • Controlling multiple substrates and environmental factors is crucial for maximizing enzyme expression.
  • Traditional fed-batch cultivation methods may not achieve optimal yields for multiple enzymes simultaneously.

Purpose of the Study:

  • To develop a multi-objective optimization formulation for multi-substrate, multi-product fermentation.
  • To apply the epsilon-constraint method for trade-off solutions between enhancing one product and minimizing another.
  • To experimentally validate optimal control profiles for Aspergillus niger fed-batch fermentation.

Main Methods:

  • Formulation of a multi-objective optimization problem with epsilon-constraint.

Related Experiment Videos

  • Application to fed-batch fermentation of Aspergillus niger, focusing on catalase and protease production.
  • Control of feed rates for sucrose, nitrogen source, and oxygen, including novel oxygen supply via H2O2.
  • Solution using a differential evolution algorithm.

    • Main Results:

    • Generated Pareto-optimal curves illustrating trade-off solutions.
    • Achieved approximately 70% increase in final catalase and 31% increase in final protease compared to conventional fed-batch cultivation.
    • Demonstrated successful experimental evaluation of optimal control profiles.

    Conclusions:

    • The proposed multi-objective optimization formulation effectively enhances selective enzyme production in fed-batch fermentation.
    • Novel oxygen supply methods can overcome aeration limitations.
    • The differential evolution algorithm provides a robust solution for complex optimization problems in bioprocesses.