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Related Experiment Video

Updated: Aug 17, 2025

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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Artificial intelligence technologies in bioprocess: Opportunities and challenges.

Yang Cheng1, Xinyu Bi1, Yameng Xu1

  • 1Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; Science Center for Future Foods, Ministry of Education, Jiangnan University, Wuxi 214122, China.

Bioresource Technology
|December 12, 2022
PubMed
Summary

Artificial intelligence (AI) enhances bioprocess control and optimization by integrating data-driven modeling and rapid detection technologies. This review guides future development of AI-guided bioprocess optimization and control for improved microbial metabolic potential.

Keywords:
Artificial intelligenceBioprocess modelingRapid detectionReal-time monitoringSmart control

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Area of Science:

  • Biotechnology and biochemical engineering
  • Process systems engineering
  • Microbial biotechnology

Background:

  • Bioprocess control and optimization are essential for maximizing microbial metabolic potential.
  • Advancements in computer-based strategies combined with current technologies promise significant bioprocess improvements.
  • Artificial intelligence (AI) offers a data-driven approach, independent of complex mathematical models, for bioprocess applications.

Approach:

  • This review discusses AI-guided modeling approaches for optimizing critical process parameters (CPPs) in bioprocesses.
  • It summarizes AI-assisted technologies for rapid detection and monitoring within bioprocesses.
  • The analysis includes control strategies derived from AI-guided modeling and monitoring.

Key Points:

  • AI-guided modeling optimizes critical process parameters (CPPs).
  • AI facilitates rapid detection and monitoring in bioprocesses.
  • Control strategies are analyzed based on AI-driven modeling and monitoring.

Conclusions:

  • AI integration offers a powerful pathway for advancing bioprocess optimization and control.
  • Future research should focus on addressing current gaps in AI-guided bioprocess technologies.
  • This review provides theoretical guidance for developing novel AI-guided bioprocess solutions.