[Mesoscale simulation and AI optimization of bioprocesses].

Area of Science:

• Biotechnology and Biomanufacturing
• Computational Biology
• Process Engineering

Background:

• Bioprocesses are essential for green, sustainable manufacturing using biological systems.
• Optimizing bioprocesses is challenging due to complex multi-scale and multi-level interactions.
• Understanding mesoscale behaviors is critical for elucidating bioprocess dynamics and parameter relationships.

Purpose of the Study:

• To review advancements in mesoscale simulation for bioprocesses.
• To explore the integration of artificial intelligence (AI) with mesoscale simulation for bioprocess optimization.
• To discuss future development directions in this interdisciplinary field.

Main Methods:

• Mesoscale numerical simulation to model bioprocess phenomena.
• Integration of artificial intelligence (AI) algorithms with mesoscale simulations.
• Literature review of current research and methodologies.

Main Results:

• Mesoscale simulation provides insights into complex bioprocess dynamics.
• AI-AI integration with mesoscale simulation enhances optimization capabilities.
• Identified key areas for future research and development.

Conclusions:

• The synergy between mesoscale simulation and AI is vital for advancing biomanufacturing.
• Further development is needed to fully leverage these integrated approaches for efficient bioprocess optimization.
• This review highlights the potential to accelerate innovation in sustainable bioprocessing.