Uso da calorimetria exploratória diferencial na predição de propriedades a frio do querosene de aviação

Abstract

Aviation fuels play a key role in the aircraft operations chain, requiring strict quality control procedures. Although well-established, conventional methods used to characterize the physicochemical properties of these fuels often present limitations related to analysis time, high cost, and the need for significant sample volumes. Given the growing demand for alternative methods to predict physicochemical properties, the present study aims to evaluate the application of Differential Scanning Calorimetry (DSC) as a technique for the determination of cold flow properties in aviation fuels, as well as to investigate the direct correlation between the Manual and Automatic Freezing Point methods. A set of more than 50 commercial jet fuel samples was analyzed using reference methods for determining freezing point, cloud point, and pour point, and subsequently subjected to an optimized DSC methodology. Based on direct data correlation, three models were built following approaches reported in the literature. The correlation was classified as very strong for most of the freezing point prediction models and for both cloud point models, and as strong for the pour point model. However, from a statistical standpoint, only one of the freezing point models was considered equivalent to the reference method. Thus, the use of DSC curves combined with the Partial Least Squares (PLS) chemometric method enabled the construction of predictive models for cloud point, pour point and kinematic viscosity which showed coefficients of determination (r2cal e r2val) above 0.90, RMSEP/RMSEC ratio below 2.5, and satisfactory RPD values, indicating robust performance. Furthermore, the model results were considered statistically equivalent to the reference methods, confirming their effectiveness for application in quality control analyses. The results demonstrate the potential of Differential Scanning Calorimetry, associated with PLS chemometric modeling, in the analysis of aviation fuel samples. Considering the limited number of studies focused on the prediction of cloud and pour points in this type of matrix, this work makes a significant contribution to the advancement of analytical methodologies applied to aviation fuels. Moreover, the developed strategy shows potential for extension to other fossil and renewable fuels, as well as their blends.