Digital innovations in bioprocess optimization: The role of AI, machine learning, and digital twins

Bioprocess modeling is undergoing a transformative phase with the advent of digital technologies like artificial intelligence (AI) and machine learning (ML). Historically reliant on conventional methods, the field now strives to overcome existing limitations in process control, efficiency, and scale-up for bioprocesses, including specialty chemicals and bioenergy production. Current debates focus on how these digital tools can revolutionize process analytical technologies (PAT), providing enhanced real-time data analysis and process optimization. Recent studies highlight the potential of digital twins and predictive models, yet their implementation remains in the nascent stages, highlighting a gap in both research and application.

This Research Topic aims to explore the integration of cutting-edge digital technologies within bioprocess engineering to enable sustainable industrial practices. By investigating methodologies for implementing digital twins and predictive models, this research seeks to enhance real-time decision-making and process efficiency. It will evaluate the transformative potential of these technologies on the future of bioprocess optimization, focusing on the development of advanced PAT capable of revolutionizing control, efficiency, and methodologies for scaling up bioprocesses.

To gather further insights into bioprocess modeling innovations and their applications, we welcome articles addressing, but not limited to, the following themes:

- The role of AI and ML in enhancing bioprocess control
- Development and application of digital twins in bioprocess engineering
- Predictive modeling techniques for optimization and scaling-up of bioprocess
- Case studies on successful integrations of digital technologies in bioprocesses
- Future perspectives on sustainable industrial practices through digital innovation

We invite researchers to contribute a variety of article types, including original research, reviews, and case studies, to aid in the collaborative exploration of these futuristic directions in bioprocess modeling.