Artificial intelligence technologies in bioprocess: Opportunities and challenges. (February 2023)

• Record Type: Journal Article
• Authors: Cheng, Yang; Bi, Xinyu; Xu, Yameng; Liu, Yanfeng; Li, Jianghua; Du, Guocheng; Lv, Xueqin; Liu, Long
• Is Part Of: Bioresource technology. Volume 369(2023)
• Publication Date: 2023-02
• Subjects: Artificial intelligence -- Bioprocess modeling -- Rapid detection -- Real-time monitoring -- Smart control; Biomass -- Periodicals; Biomass energy -- Periodicals; Bioremediation -- Periodicals; Agricultural wastes -- Periodicals; Factory and trade waste -- Periodicals; Organic wastes -- Periodicals; Bioénergie -- Périodiques; Déchets agricoles -- Périodiques; Déchets industriels -- Périodiques; Déchets organiques -- Périodiques; Déchets (Combustible) -- Périodiques; 662.88
• DOI: 10.1016/j.biortech.2022.128451
• Languages: English
• ISSNs: 0960-8524

Abstract

Graphical abstract: Highlights: AI has been extensively applied in bioprocess modeling and optimization. Convenient, accurate and rapid monitoring technologies are emerging. Advanced control strategies based on AI technologies have been proposed. Integrating AI with GEMs and CFD will further improve bioprocess. Abstract: Bioprocess control and optimization are crucial for tapping the metabolic potential of microorganisms, and which have made great progress in the past decades. Combination of the current control and optimization technologies with the latest computer-based strategies will be a worth expecting way to improve bioprocess further. Recently, artificial intelligence (AI) emerged as a data-driven technique independent of the complex interactions used in mathematical models and has been gradually applied in bioprocess. In this review, firstly, AI-guided modeling approaches of bioprocess are discussed, which are widely applied to optimize critical process parameters (CPPs). Then, AI-assisted rapid detection and monitoring technologies employed in bioprocess are summarized. Next, control strategies according to the above two technologies in bioprocess are analyzed. Lastly, current research gaps and future perspectives on AI-guided optimization and control technologies are discussed. This review provides theoretical guidance for developing AI-guided bioprocess optimization and control technologies.