Thermophysical properties of biofuel systems under high-pressure conditions

Abstract

The growing demand for sustainable alternatives to fossil fuels presents technical and

scientific challenges related to the use of biofuels, particularly in understanding their thermophysical behavior under real operating conditions, such as high pressure. This thesis provides a comprehensive investigation of the thermophysical properties of biodiesel-based systems, encompassing both pure fuels and blends with 1-butanol and diesel. The objective is to address gaps in the literature, evaluate the feasibility of alternative formulations, and propose advances in predictive modeling. Properties such as density, speed of sound, isobaric heat capacity, and distillation profile were analyzed, along with derived properties such as compressibility, thermal expansivity, and excess properties. Experimental measurements were conducted over a wide pressure range, from atmospheric conditions to levels representative of the real operating environment of internal combustion engines, which can reach pressures up to 200 MPa. The addition of 1-butanol was explored as a strategy to mitigate the limitations of biodiesel, including its low volatility and poor performance at reduced temperatures, highlighting the potential of this alcohol as a promising renewable additive. In addition to generating novel experimental data, the study incorporates various modeling approaches, including the PC-SAFT equation of state, corresponding states models, and empirical correlations based on fatty ester composition, with the aim of improving the accuracy of property predictions. The results indicate that, although the evaluated models perform satisfactorily in specific contexts, targeted adaptations and parameterizations are essential for the accurate representation of multicomponent mixtures under high pressure. This work makes a significant contribution to the advanced characterization of biofuels, supporting the development of more efficient and sustainable fuels and enhancing modeling tools in process engineering.