Artificial intelligence technologies in bioprocess: Opportunities and challenges

被引：30
作者：
Cheng, Yang [1 ,2 ]
Bi, Xinyu [1 ,2 ]
Xu, Yameng [1 ,2 ]
Liu, Yanfeng [1 ,2 ]
Li, Jianghua [1 ,2 ]
Du, Guocheng [1 ,2 ]
Lv, Xueqin [1 ,2 ]
Liu, Long [1 ,2 ]
机构：
[1] Jiangnan Univ, Key Lab Carbohydrate Chem & Biotechnol, Minist Educ, Wuxi 214122, Peoples R China
[2] Jiangnan Univ, Sci Ctr Future Foods, Minist Educ, Wuxi 214122, Peoples R China
来源：
BIORESOURCE TECHNOLOGY | 2023年 / 369卷
关键词：
Artificial intelligence; Bioprocess modeling; Rapid detection; Real-time monitoring; Smart control; NEURAL-NETWORK; GENETIC ALGORITHM; FEEDBACK-CONTROL; OPTIMIZATION; FERMENTATION; GROWTH; WASTE; PARAMETERS; SENSORS;
D O I：
10.1016/j.biortech.2022.128451
中图分类号：
S2 [农业工程];
学科分类号：
0828 ;
摘要：
Bioprocess control and optimization are crucial for tapping the metabolic potential of microorganisms, 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 further. Recently, artificial intelligence (AI) emerged as a data-driven technique independent of the interactions used in mathematical models and has been gradually applied in bioprocess. In this review, guided modeling approaches of bioprocess are discussed, which are widely applied to optimize critical parameters (CPPs). Then, AI-assisted rapid detection and monitoring technologies employed in bioprocess summarized. Next, control strategies according to the above two technologies in bioprocess are analyzed. current research gaps and future perspectives on AI-guided optimization and control technologies are This review provides theoretical guidance for developing AI-guided bioprocess optimization and technologies.
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页数：12
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