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

Mondal PP 1 , Galodha A 2 , Verma VK 1 , Singh V 3 , Show PL 4 , Awasthi MK 5 Show all authors , Lall B 6 , Anees S 7 , Pollmann K 8 , Jain R 9

Affiliations 

  • 1 Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, Hauz-Khas, New Delhi 110016, India
  • 2 School of Interdisciplinary Research, Indian Institute of Technology Delhi, Hauz-Khas, New Delhi 110016, India
  • 3 Department of Biosciences, School of Science, Indrashil University, Rajpur, Mehsana, 382715, Gujarat, India
  • 4 Zhejiang Provincial Key Laboratory for Subtropical Water Environment and Marine Biological Resources Protection, Wenzhou University, Wenzhou 325035, China; Department of Sustainable Engineering, Saveetha School of Engineering, SIMATS, Chennai 602105, India; Department of Chemical and Environmental Engineering, University of Nottingham, Malaysia, 43500 Semenyih, Selangor Darul Ehsan, Malaysia
  • 5 College of Natural Resources and Environment, Northwest A&F University, Yangling, Shaanxi Province 712100, China
  • 6 Electrical Engineering Department, Indian Institute of Technology Delhi, Hauz-Khas, New Delhi 110016, India
  • 7 Department of Electronics and Communication Engineering, Indian Institute of Information Technology Guwahati, Bongora, Guwahati 781015, India
  • 8 Helmholtz-Zentrum Dresden-Rossendorf, Helmhholtz Institute Freiberg for Resource Technology, Bautzner Landstrasse 400, 01328 Dresden, Germany
  • 9 Helmholtz-Zentrum Dresden-Rossendorf, Helmhholtz Institute Freiberg for Resource Technology, Bautzner Landstrasse 400, 01328 Dresden, Germany. Electronic address: r.jain@hzdr.de
Bioresour Technol, 2023 Feb;370:128523.
PMID: 36565820 DOI: 10.1016/j.biortech.2022.128523

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.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.

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