Big data and machine learning driven bioprocessing – Recent trends and critical analysis

Chao Tung Yang, Endah Kristiani, Yoong Kit Leong, Jo Shu Chang

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)

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.

Original languageEnglish
Article number128625
JournalBioresource technology
Volume372
DOIs
Publication statusPublished - 2023 Mar

All Science Journal Classification (ASJC) codes

  • Bioengineering
  • Environmental Engineering
  • Renewable Energy, Sustainability and the Environment
  • Waste Management and Disposal

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