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. [Display omitted] • Sustainable alternative fuel (SAF) emits fewer greenhouse emissions. • Biofuel from non-edible sources is the key to achieving 100％ SAF. • Sustainable catalysts help fully cleaner production of high-quality biofuel. • Machine learning will model and predict fuel performance in the assessment stages. • The smart production of SAF coupling with machine learning reduces its costs. [ABSTRACT FROM AUTHOR]