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• W4386789378 endingPage "140191" @default.
• W4386789378 startingPage "140191" @default.
• W4386789378 abstract "Bio-alcohols have been proven promising alternatives to fossil fuels. Machine learning (ML), as an analytical tool for uncovering intrinsic correlations and mining data connotations, is also becoming widely used in the field of bio-alcohols. This article reviews the mechanisms, methods, and applications of ML in the bio-alcohols field. In terms of mechanisms, we describe the workflow of ML applications, emphasizing the importance of a well-defined research problem and complete feature engineering for a robust model. Prediction and optimization are the main application scenarios. In terms of methods, we illustrate the characteristics of different ML models and analyze their applicability in the bio-alcohol field. The role of ML in the production of bio-methanol by pyrolysis and gasification, as well as in the three stages of fermentation for bioethanol production are highlighted. In terms of utilization, ML is used to optimize engine performance and reduce emissions. This review provides guidance on how to use novel ML methods in the bio-alcohol field, showing the potential of ML to streamline work in the whole biofuel field." @default.
• W4386789378 created "2023-09-16" @default.
• W4386789378 creator A5021210018 @default.
• W4386789378 creator A5028931040 @default.
• W4386789378 creator A5057590058 @default.
• W4386789378 creator A5092088263 @default.
• W4386789378 date "2023-11-01" @default.
• W4386789378 modified "2023-10-03" @default.
• W4386789378 title "Mechanisms, methods and applications of machine learning in bio-alcohol production and utilization: A review" @default.
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• W4386789378 doi "https://doi.org/10.1016/j.chemosphere.2023.140191" @default.
• W4386789378 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37716556" @default.
• W4386789378 hasPublicationYear "2023" @default.
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