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Related Experiment Videos

Bioprocess supervision: neural networks and knowledge based systems

J Glassey1, M Ignova, A C Ward

  • 1Department of Chemical and Process Engineering, University of Newcastle upon Tyne, United Kingdom.

Journal of Biotechnology
|January 20, 1997
PubMed
Summary

Artificial intelligence (AI) enhances control of complex bioprocesses. AI methods like artificial neural networks improve productivity and reduce variability in bioindustries.

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Area of Science:

  • Biotechnology
  • Process Engineering
  • Artificial Intelligence

Background:

  • Bioprocesses are often highly non-linear and time-variant, posing significant control challenges.
  • Improved process supervision is crucial for enhancing productivity and reducing variability in bioindustries.

Purpose of the Study:

  • To explore the application of artificial intelligence methodologies for supervising non-linear and time-variant bioprocesses.
  • To demonstrate the potential of AI in improving bioprocess efficiency and consistency.

Main Methods:

  • Utilizing artificial neural networks (ANNs) for process modeling and prediction.
  • Employing knowledge-based systems (KBS) for intelligent decision-making and control.
  • Integrating ANNs and KBS for comprehensive bioprocess supervision.

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Main Results:

  • Demonstrated significant improvements in controlling non-linear bioprocess dynamics.
  • Achieved enhanced productivity through optimized process parameters.
  • Reduced process variability, leading to more consistent product yield and quality.

Conclusions:

  • Artificial intelligence methodologies offer powerful tools for effective bioprocess supervision.
  • ANNs and KBS integration can significantly advance the automation and optimization of bioindustrial processes.