Advanced Navy propulsion and power technologies will place increasing demands on Navy mobility fuels. The performance of fuels can be adversely affected by chemical reactions initiated by trace levels of organic compounds containing heteroatomic species such as sulfur, nitrogen, and oxygen in addition to certain aromatic and unsaturated compounds. Fuels can undergo free-radical autoxidative processes, as well as acid and base catalyzed reactions. Trace levels of catalytic metals can also impact the degree to which some fuels will undergo autoxidative degradation. Areas of continuing interest to the Navy include the identification and characterization of key reactive compounds and the sequence of reactions that can lead to the reduction of critical fuel properties, including the formation of insoluble reaction products. In order to develop methods to predict the response of a fuel to catalytic metals, the mechanism by which these metals are acquired by and bound in fuels needs to be resolved.

Currently, fuel quality assessment is performed with a suite of wet chemical and instrumental analyses. An instrumented sensing approach to fuel quality assurance offers significant advantages in analysis time, reduced operating cost, and possibly increased accuracy. The Navy advanced fuel sensor development initiative focuses on the development of automated off-line and in-line fuel quality diagnostics and prognostics. Optical and spectroscopic methods are generally the method of choice for in-line fuel quality sensors because of the relative simplicity of instrumentation, rapid analysis time, and high quality of the data from a chemometric perspective. Chemometric multivariate analysis of spectroscopic data is desirable due to several reasons. The chief reason is the so-called first order advantage, providing the ability to recognize the presence of interferants, calibrate in the presence of known interferants, and conferring the advantages normally provided by signal averaging. Relatively few fuel properties have been successfully predicted from NIR measurements. To develop adequate predictive models for the range of fuel properties required for fuel acceptance, we envision that this will require the implementation of data fusion modeling strategies that combine measurement data from an array of multiple complementary measurements, such as optical, NIR, FTIR, and other specialized sensor technologies.