| Autor: |
Mondal, P. P., Galodha, A., Verma, V. K., Singh, V., Show, P. L., Awasthi, M. K., Lall, B., Aness, S., Pollmann, K., Jain, R. |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Bioresource Technology 370(2023), 128523 |
| Popis: |
Machine Learning and Artificial intelligence are quickly becoming impending game changers for bioprocessing development. However, its true potential has not been harnessed, and real-time application is still in its interim stage to control most cognitive tasks. Hence, it is imperative to know the state of technology to identify the gaps in the knowledge. In this review, we first give an insight into the basic understanding of the machine learning domain and discuss its complexities for more comprehensive applications. Subsequently, we outline how relevant machine learning models are used to statistically and logically analyze the big datasets generated in the bioprocessing industries to control process operations. While doing so, we provide the state of technology applied in different subfields of the bioprocessing industry. Further, this review also discusses the adoption of hybrid modeling strategies for combining mechanistic models with historical data-driven machine learning models to develop new digital biotechnologies. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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