Usage of Digital Twins Along a Typical Process Development Cycle

Area of Science:

• Biotechnology
• Process Engineering
• Data Science

Background:

• Classical bioprocess development relies on trial-and-error methods, limiting optimization potential.
• Integrating digital tools (hardware, software, data, humans) offers a path to quantitative bioprocess engineering.
• Digital twins are central to interconnecting physical processes with digital frameworks.

Purpose of the Study:

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

Main Methods:

• Utilizing a digital twin framework for an exemplary microbial cultivation process.
• Applying digital twin concepts from strain characterization to real-time control.
• Continuously adapting digital twin models with newly acquired process data.

Main Results:

• Digital twin-guided development transforms trial-and-error bioprocessing into a quantitative engineering approach.
• Digital twins effectively integrate diverse stages of the bioprocess development cycle.
• The continuous adaptation of digital twins enhances their utility as knowledge management systems.

Conclusions:

• Digital twins are powerful tools for efficient bioprocess development and optimization.
• Integrating digital twins facilitates a quantitative, data-driven engineering approach to bioprocessing.
• Real-time deployment of digital twins enables targeted control actions for improved process performance.