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Hybrid physics-based and data-driven modeling for bioprocess online simulation and optimization.

Dongda Zhang1,2, Ehecatl Antonio Del Rio-Chanona2, Panagiotis Petsagkourakis1,3

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Summary
This summary is machine-generated.

This study introduces a hybrid modeling framework for bioprocess optimization, enhancing online monitoring and prediction. The innovative approach achieves near-optimal results for microalgal production, showing industrial applicability.

Keywords:
bioprocess optimizationdata recalibrationfed-batch operationkinetic modelingmachine learning

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Area of Science:

  • Bioprocess Engineering
  • Computational Biology
  • Chemical Engineering

Background:

  • Model-based online optimization is underutilized in bioprocesses due to complex biological modeling, poor data quality, and limited real-time visualization.
  • Existing methods struggle with the dynamic and intricate nature of biological systems.

Purpose of the Study:

  • To develop and validate a novel hybrid modeling framework for enhanced bioprocess online monitoring, prediction, and optimization.
  • To address the limitations of current bioprocess control strategies.

Main Methods:

  • A hybrid framework combining physics-based and data-driven models for data correction and prediction.
  • Utilizing a physics-based noise filter for raw data enhancement.
  • Employing a data-driven model for optimal control action identification and future state prediction.
  • Implementing a soft sensor approach for continuous process visualization.

Main Results:

  • The proposed framework successfully optimized fed-batch microalgal lutein production.
  • Achieved optimal results comparable to theoretical maximums.
  • Demonstrated high predictive accuracy and flexibility in monitoring ongoing processes.

Conclusions:

  • The hybrid modeling framework offers a robust solution for complex bioprocess challenges.
  • It enables accurate real-time monitoring, prediction, and optimization, with significant potential for industrial adoption.