Artificial intelligence and machine learning for smart bioprocesses.

Journal: Bioresource technology
Published Date:

Abstract

In recent years, the digital transformation of bioprocesses, which focuses on interconnectivity, online monitoring, process automation, artificial intelligence (AI) and machine learning (ML), and real-time data acquisition, has gained considerable attention. AI can systematically analyze and forecast high-dimensional data obtained from the operating dynamics of bioprocess, allowing for precise control and synchronization of the process to improve performance and efficiency. Data-driven bioprocessing is a promising technology for tackling emerging challenges in bioprocesses, such as resource availability, parameter dimensionality, nonlinearity, risk mitigation, and complex metabolisms. This special issue entitled "Machine Learning for Smart Bioprocesses (MLSB-2022)" was conceptualized to incorporate some of the recent advances in applications of emerging tools such as ML and AI in bioprocesses. This VSI: MLSB-2022 contains 23 manuscripts, and summarizes the major findings that can serve as a valuable resource for researchers to learn major advances in applications of ML and AI in bioprocesses.

Authors

  • Samir Kumar Khanal
    Department of Molecular Biosciences and Bioengineering, University of Hawai'i at Mānoa, Honolulu, HI 96822, USA; Department of Civil and Environmental Engineering, University of Hawai'i at Mānoa, Honolulu, HI 96822, USA. Electronic address: khanal@hawaii.edu.
  • Ayon Tarafdar
    Livestock Production & Management Section, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly 243 122, Uttar Pradesh, India.
  • Siming You
    James Watt School of Engineering, University of Glasgow, G12 8QQ Glasgow, UK.