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Artificial intelligence and machine learning for smart bioprocesses
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.
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Researchers are concerned about enormous power and storage consumption caused by ADT processes, particularly immense computations needed for DD models and ADT architecture with huge IoT networks.
“Researchers are concerned about the enormous power and storage consumption caused by ADT processes, particularly when it comes to the immense quantities of computations needed for DD models as well as the ADT architecture with its huge IoT networks [72,80].”
From Reinvigorating algal cultivation for biomass production with digital twin technology - a smart sustainable infrastructure (2024) · cites this paper as [72]
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Metadata
- Authors
- Khanal, S. K., Tarafdar, A., & You, S.
- Affiliations
- University of Hawaiʻi at Mānoa (US)
- Indian Veterinary Research Institute (IN)
- University of Glasgow (GB)
- Corresponding author
- Khanal, Samir Kumar
- Venue
- Bioresource Technology
- Publisher
- Elsevier BV
- Published
- 2023-05
- Volume
- 375
- Pages
- 128826
- ISSN
- 0960-8524 (ISSN-L)
- OpenAlex
- W4323275667
- Cited by
- 86
- References
- 23
- Citations by year
- 2026: 28 · 2025: 25 · 2024: 20 · 2023: 13
- FWCI
- 9.24
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Bioresource Technology (publishedVersion)
- PubMed (publishedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
- Keywords
- artificial intelligence, machine learning, bioprocessing, digital transformation, process automation, online monitoring, real-time data acquisition, bioprocess control
- MeSH terms
- Machine Learning, Artificial Intelligence
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