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Combining model structure identification and hybrid modelling for photo-production process predictive simulation and

Dongda Zhang1, Thomas R Savage1, Bovinille A Cho1

  • 1Centre for Process Integration, University of Manchester, The Mill, Manchester, UK.

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Summary

This study introduces a hybrid model combining kinetic and data-driven approaches for microalgal photo-production. This method accurately simulates and optimizes algal lutein production, overcoming data limitations.

Keywords:
automatic model identificationbioprocess optimisationfed‐batch operationhybrid modellingmachine learning

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Area of Science:

  • * Biotechnology and biochemical engineering
  • * Computational biology and bioinformatics
  • * Process systems engineering

Background:

  • * Digital twins are crucial for optimizing microalgal and cyanobacterial photo-production.
  • * Kinetic models face challenges in capturing complex biological mechanisms.
  • * Data-driven models are hindered by limited experimental data.

Purpose of the Study:

  • * To develop an innovative hybrid modeling approach for photo-production processes.
  • * To address challenges in kinetic model structure identification and data scarcity.
  • * To create a highly accurate mathematical model for algal lutein production.

Main Methods:

  • * A hybrid model combining a kinetic model with a data-driven model to estimate discrepancies.
  • * Advanced automatic model structure identification for kinetic and data-driven components.
  • * Validation using multiple datasets across various operating conditions.

Main Results:

  • * A highly accurate mathematical model was constructed for algal lutein production.
  • * The hybrid model demonstrated effective long-term predictive modeling and optimization.
  • * Successful online self-calibration capabilities were shown.

Conclusions:

  • * The proposed hybrid modeling framework overcomes limitations of traditional approaches.
  • * This approach offers significant potential for industrial applications in microalgal photo-production.
  • * Accurate simulation and optimization of algal lutein production were achieved.