A surrogate fuel emulating the physical and chemical properties of aviation biofuels

Abstract

The development of aviation biofuels is a key strategy for reducing carbon emissions in the aviation industry. This study aimed to establish a surrogate model for aviation biofuels using a hybrid approach that combined explicit equations with an artificial neural network (ANN). The low heating value was calculated using an explicit equation, whereas the ANN predicted changes in density, viscosity, surface tension with temperature, and the distillation curve of the surrogate model. An optimization algorithm was then employed to identify suitable substitutes, which consisted of 11.44% n-decane, 43.43% n-dodecane, 43.11% n-tetradecane, and 2.02% methylcyclohexane. The maximum error between the physical properties of the surrogate components and the measured biofuels did not exceed 7%. The ignition delay time of the substitute components matched that of real aviation biofuels at an equivalence ratio of 1.0 and a pressure of 10 bar.