Smart aviation biofuel energy system coupling with machine learning technology

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.

Automated summary

The excessive use of fossil energy globally has led to an increase in greenhouse gas emissions. Sustainable aviation fuels (SAFs) are proposed as a promising solution to mitigate these emissions in the aviation sector. However, the high costs of current SAFs remain a challenge, and machine learning-based technologies are important for developing intelligent aviation fuel systems in the future. This research aims to systematically summarize the latest findings on machine learning technology in smart biofuel energy systems.