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Related Experiment Videos

Can imperfections help to improve bioreactor performance?

Pratap R Patnaik1

  • 1Institute of Microbial Technology, Chandigarh, India. pratap@imtech.res.in

Trends in Biotechnology
|March 22, 2002
PubMed
Summary

Harnessing bioreactor noise and incomplete mixing using artificial neural networks improves fermentation productivity. This approach outperforms traditional methods by leveraging non-ideal conditions for better results in recombinant fermentation.

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Area of Science:

  • Biotechnology
  • Biochemical Engineering
  • Process Control

Background:

  • Pilot-scale and larger bioreactors exhibit significant noise and incomplete broth mixing, deviating from ideal laboratory conditions.
  • Conventional bioprocess control strategies aim to minimize these non-ideal factors, often leading to suboptimal performance.
  • Exploiting these inherent non-ideal characteristics presents an alternative approach to enhance bioprocess outcomes.

Purpose of the Study:

  • To investigate the potential of artificial neural networks (ANNs) for controlling non-ideal bioreactor conditions.
  • To determine if exploiting noise and incomplete mixing can improve recombinant fermentation performance.
  • To compare the productivity of ANNs-controlled non-ideal operations with conventional well-mixed, noise-free operations.

Main Methods:

Related Experiment Videos

  • Development and application of artificial neural networks for on-line control of bioreactor parameters.
  • Monitoring and control of broth mixing, noise levels, and plasmid copy number distribution.
  • Comparative analysis of fermentation productivity under different control strategies.

Main Results:

  • Artificial neural networks effectively controlled the degree of mixing and noise filtering on-line.
  • The ANNs strategy enabled effective control over plasmid copy number distribution in recombinant fermentation.
  • Productivity achieved through the ANNs-controlled non-ideal operation surpassed that of well-mixed, noise-free operations.

Conclusions:

  • Deviations from ideal bioreactor behavior, such as noise and incomplete mixing, can be effectively harnessed for improved performance.
  • Artificial neural networks offer a powerful tool for on-line control and optimization of non-ideal bioprocesses.
  • Future bioprocess development should consider exploiting, rather than suppressing, non-ideal operational features for enhanced productivity.