Development and optimization of alternative fuels for aviation and turbomachinery

This study focuses on the development and optimization of alcohol-based alternative fuel bases for aviation and turbomachinery to reduce carbon emissions. Volumetric experiments were conducted to measure thermal stability through pressure changes in a sealed vessel, complemented by calculations of heat of combustion, viscosity, and density of fuel blend bases. Neural networks were employed to predict thermal destruction rate constants, physicochemical properties, and other missing parameters. Multi-criteria optimization, using CAE systems and the Weighted Sum Model, was applied to select optimal base compositions. Bases of n-propanol-1, n-butanol-1, isopropanol, and isobutanol with JetA-1 aviation fuel blend base were evaluated. The optimal base, containing 15% n-butanol-1, achieved a 1.2% reduction in CO₂ emissions (3.12 kg CO₂/kg base) and a 2.7% decrease in viscosity (2.92 cSt), with a 3.4% decrease in lower heating value (41.51 MJ/kg). The integration of neural networks with optimization represents a novel approach to developing sustainable fuel bases, balancing performance and environmental requirements. These findings are applicable to short-haul flights where emission reduction is prioritized.