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Big data and machine learning driven bioprocessing - Recent trends and critical analysis.

Chao-Tung Yang1, Endah Kristiani2, Yoong Kit Leong3

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Summary

Artificial intelligence (AI) is revolutionizing bioengineering. This study analyzed AI in bioprocessing from 2013-2022, finding AI enhances design and engineering strategies in bioprocessing fields.

Keywords:
Artificial intelligenceArtificial neural networksBioprocessingHybrid modelsMachine learningNatural language processing

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Area of Science:

  • Bioengineering
  • Biotechnology
  • Process Engineering

Background:

  • Machine learning algorithms offer transformative potential in bioengineering.
  • Artificial intelligence (AI) applications in bioprocessing are rapidly expanding.
  • Understanding research trends in AI for bioprocessing is crucial for future advancements.

Purpose of the Study:

  • To examine and summarize the literature on artificial intelligence (AI) in the bioprocessing field.
  • To explore research directions using Natural Language Processing (NLP).
  • To compare research trends in bioprocessing using AI between 2013-2017 and 2018-2022.

Main Methods:

  • Literature review of AI in bioprocessing from 2013-2022 using Scopus database.
  • Keyword-based extraction and grouping of publications into two five-year periods.
  • Natural Language Processing (NLP) for domain direction analysis.
  • Review and analysis of selected papers from the recent five-year period.

Main Results:

  • Analysis revealed a shift in research focus over the decade.
  • Fifty percent of publications in the recent five-year period concentrated on hybrid models, Artificial Neural Networks (ANN), biopharmaceutical manufacturing, and biorefinery.
  • AI implementation shows significant potential for improving bioprocessing design and engineering.

Conclusions:

  • AI is a key driver for innovation in the bioprocessing field.
  • The integration of AI, particularly hybrid models and ANN, is crucial for advancing biopharmaceutical manufacturing and biorefinery processes.
  • AI adoption can lead to optimized design and process engineering strategies, enhancing overall efficiency in bioprocessing.