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Updated: Nov 24, 2025

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Usage of Digital Twins Along a Typical Process Development Cycle.

Peter Sinner1, Sven Daume1, Christoph Herwig1,2

  • 1Institute of Chemical, Environmental and Bioscience Engineering, Technische Universität Wien, Vienna, Austria.

Advances in Biochemical Engineering/Biotechnology
|December 21, 2020
PubMed
Summary
This summary is machine-generated.

Digital twins enable a quantitative engineering approach to bioprocess development, moving beyond trial-and-error. This technology integrates hardware, software, and data for enhanced process optimization and real-time control.

Keywords:
Bioprocess developmentControlDigital twinDynamic modelingProcess systems engineeringReal-time monitoring

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Area of Science:

  • Biotechnology
  • Process Engineering
  • Computational Biology

Background:

  • Classical bioprocess development relies on trial-and-error methods.
  • Digitalization offers potential for quantitative process design, monitoring, and control.
  • Integrating hardware, software, data, and human expertise is key to unlocking process optimization.

Purpose of the Study:

  • To demonstrate the application of digital twin-guided process development.
  • To illustrate the integration of digital twins throughout the bioprocess development lifecycle.
  • To highlight the role of digital twins in knowledge management and decision support.

Main Methods:

  • Development and application of a digital twin for a microbial cultivation process.
  • Utilizing continuous data adaptation to refine digital twin models.
  • Implementing real-time control strategies based on digital twin insights.

Main Results:

  • Digital twins can guide bioprocess development from strain characterization to real-time control.
  • Continuous model adaptation enhances the digital twin's integration across the entire process cycle.
  • Digital twins serve as effective knowledge management and decision support systems.

Conclusions:

  • Digital twin technology transforms bioprocess development into a quantitative engineering discipline.
  • Digital twins facilitate efficient process optimization and real-time control.
  • The adaptive nature of digital twins is crucial for comprehensive process integration and performance improvement.