Big data and machine learning driven bioprocessing – Recent trends and critical analysis. (March 2023)

• Record Type: Journal Article
• Authors: Yang, Chao-Tung; Kristiani, Endah; Leong, Yoong Kit; Chang, Jo-Shu
• Is Part Of: Bioresource technology. Volume 372(2023)
• Publication Date: 2023-03
• Subjects: Artificial intelligence -- Bioprocessing -- Machine learning -- Hybrid models -- Artificial neural networks -- Natural language processing; 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.2023.128625
• Languages: English
• ISSNs: 0960-8524

Abstract

Graphical abstract: Highlights: Hybrid model, ANN, biopharmaceutical, biorefinery contribute 50% of research topics. Hybrid model experiments produce useful findings in bioprocessing. The complexity in bioprocessing experiments is suitable to solve by ANN algorithm. Biopharmaceutical is impacted by advances biologics, digital age, and COVID-19. Abstract: Given the potential of machine learning algorithms in revolutionizing the bioengineering field, this paper examined and summarized the literature related to artificial intelligence (AI) in the bioprocessing field. Natural language processing (NLP) was employed to explore the direction of the research domain. All the papers from 2013 to 2022 with specific keywords of bioprocessing using AI were extracted from Scopus and grouped into two five-year periods of 2013-to-2017 and 2018-to-2022, where the past and recent research directions were compared. Based on this procedure, selected sample papers from recent five years were subjected to further review and analysis. The result shows that 50% of the publications in the past five-year focused on topics related to hybrid models, ANN, biopharmaceutical manufacturing, and biorefinery. The summarization and analysis of the outcome indicated that implementing AI could improve the design and process engineering strategies in bioprocessing fields.