Artificial intelligence and machine learning for smart bioprocesses. (May 2023)

• Record Type: Journal Article
• Authors: Khanal, Samir Kumar; Tarafdar, Ayon; You, Siming
• Is Part Of: Bioresource technology. Volume 375(2023)
• Publication Date: 2023-05
• Subjects: Artificial intelligence -- Machine learning -- Smart bioprocess -- Hybrid modeling -- Digital transformation; 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.2023.128826
• Languages: English
• ISSNs: 0960-8524

Abstract

Abstract: In recent years, the digital transformation of bioprocesses, which focuses on interconnectivity, online monitoring, process automation, artificial intelligence (AI) and machine learning (ML), and real-time data acquisition, has gained considerable attention. AI can systematically analyze and forecast high-dimensional data obtained from the operating dynamics of bioprocess, allowing for precise control and synchronization of the process to improve performance and efficiency. Data-driven bioprocessing is a promising technology for tackling emerging challenges in bioprocesses, such as resource availability, parameter dimensionality, nonlinearity, risk mitigation, and complex metabolisms. This special issue entitled "Machine Learning for Smart Bioprocesses (MLSB-2022) " was conceptualized to incorporate some of the recent advances in applications of emerging tools such as ML and AI in bioprocesses. This VSI: MLSB-2022 contains 23 manuscripts, and summarizes the major findings that can serve as a valuable resource for researchers to learn major advances in applications of ML and AI in bioprocesses.