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Comparison of physics-based and data-driven modelling techniques for dynamic optimisation of fed-batch bioprocesses.

Ehecatl Antonio Del Rio-Chanona1, Nur Rashid Ahmed2, Jonathan Wagner3

  • 1Centre for Process Systems Engineering, Imperial College London, South Kensington Campus, London, UK.

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|July 31, 2019
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Summary

This study compared physics-based and data-driven models for optimizing microalgal lutein production. Data-driven models proved more effective for dynamic bioprocess optimization and prediction in industrial bio-manufacturing.

Keywords:
artificial neural networkdynamic optimisationfed-batch operationkinetic modellingmachine learning

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Area of Science:

  • Biotechnology
  • Process Engineering
  • Computational Biology

Background:

  • Digital bioprocessing is essential for modern industrial bioprocesses.
  • Optimizing long-term bioprocesses requires advanced modeling techniques.
  • Microalgal lutein production is a key area in industrial biotechnology.

Purpose of the Study:

  • To investigate the efficiency of physics-based versus data-driven models for dynamic bioprocess optimization.
  • To compare the predictive accuracy and optimization performance of these models for microalgal lutein production.
  • To evaluate the potential of data-driven models in industrial bio-manufacturing.

Main Methods:

  • Developed and employed a predictive kinetic (physics-based) model and a data-driven model.
  • Utilized fed-batch operation for microalgal lutein production.
  • Applied open-loop optimization strategies using light intensity and nitrate inflow rate as control variables.
  • Employed various optimization algorithms to compute optimal control sequences.
  • Conducted experimental verification to compare model predictions with actual results.

Main Results:

  • Physics-based and data-driven models yielded contradictory optimization strategies.
  • The data-driven model demonstrated higher predictive accuracy compared to the physics-based model.
  • Both models increased intracellular lutein content by over 40%.
  • The data-driven model achieved a 40-50% increase in total lutein production, outperforming the kinetic model.

Conclusions:

  • Data-driven modeling offers advantages for optimizing and predicting complex dynamic bioprocesses.
  • The data-driven approach shows significant potential for industrial bio-manufacturing systems.
  • Experimental validation confirmed the superiority of the data-driven model in this specific application.