Review on machine learning-based bioprocess optimization, monitoring, and control systems

Partha Pratim Mondal, Abhinav Galodha, Vishal Kumar Verma, Vijai Singh, Pau Loke Show, Mukesh Kumar Awasthi, Brejesh Lall, Sanya Anees, Katrin Pollmann, Rohan Jain

Research output: Contribution to journalReview articlepeer-review

24 Scopus citations

Abstract

Machine Learning is quickly becoming an impending game changer for transforming big data thrust from the bioprocessing industry into actionable output. However, the complex data set from bioprocess, lagging cyber-integrated sensor system, and issues with storage scalability limit machine learning real-time application. Hence, it is imperative to know the state of technology to address prevailing issues. This review first gives an insight into the basic understanding of the machine learning domain and discusses its complexities for more comprehensive applications. Followed by an outline of how relevant machine learning models are for statistical and logical analysis of the enormous datasets generated to control bioprocess operations. Then this review critically discusses the current knowledge, its limitations, and future aspects in different subfields of the bioprocessing industry. Further, this review discusses the prospects of adopting a hybrid method to dovetail different modeling strategies, cyber-networking, and integrated sensors to develop new digital biotechnologies.

Original languageBritish English
Article number128523
JournalBioresource Technology
Volume370
DOIs
StatePublished - Feb 2023

Keywords

  • Biofuels
  • Biological water treatment
  • Biopharmaceuticals
  • Machine learning
  • Modeling

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