Smart aviation biofuel energy system coupling with machine learning technology

Xin He, Ning Wang, Qiaoqiao Zhou, Jun Huang, Seeram Ramakrishna and Fanghua Li

Renewable and Sustainable Energy Reviews, 2024, vol. 189, issue PB

Abstract: The global excessive use of fossil energy has led to a sharp rise of greenhouse gas (GHG) in the atmosphere. In the fast-growing aviation sector, the global aviation industry has already made efforts to address the growing GHG emissions, in which sustainable aviation fuels (SAFs) are proposed as a promising solution to mitigate GHG emissions. However, the costs of current SAFs are still higher when compared to conventional fossil jet fuels. Therefore, reducing costs and increasing product competitiveness are currently the biggest challenges to SAFs. Machine learning-based technologies, which can shorten technology development time and support process optimization, are important techniques to achieve intelligent systems of certified aviation fuels in the future. Given that these machine learning and future biofuel systems are the focus of much scientific, this research will systematically summarize the latest relevant research findings. Up to now, comprehensive review of machine learning technology in smart biofuel energy systems is still lacking. Therefore, this research reviewed the smart biofuel energy system coupling with machine learning technology for aviation biofuel applications. We believe that this review will be of particular interest to chemists and chemical engineers in the biofuel community, in addition to researchers working on machine learning.

Keywords: Sustainable aviation fuel; Machine learning; Microalgae; Sustainable catalyst; Smart biofuel energy system; Bioeconomy (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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DOI: 10.1016/j.rser.2023.113914

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