Analysis of Twenty-Two Performance Properties of Diesel, Gasoline, and Jet Fuels Using a Field-Portable Near-Infrared (NIR) Analyzer. Issue 5 (May 2016) - Brouillette, Carl; Smith, Wayne; Shende, Chetan; Gladding, Zack; Farquharson, Stuart; Morris, Robert E.; Cramer, Jeffrey A.; Schmitigal, Joel
The change in custody of fuel shipments at depots, pipelines, and ports could benefit from an analyzer that could rapidly verify that properties are within specifications. To meet this need, the design requirements for a fuel analyzer based on near-infrared (NIR) spectroscopy, such as spectral region and resolution, were examined. It was found that the 1000 to 1600 nm region, containing the second CH overtone and combination vibrational modes of hydrocarbons, provided the best near-infrared to fuel property correlations when path length was taken into account, whereas 4 cm −1 resolution provided only a modest improvement compared to 16 cm −1 resolution when four or more latent variables were used. Based on these results, a field-portable near-infrared fuel analyzer was built that employed an incandescent light source, sample compartment optics to hold 2 mL glass sample vials with ∼1 cm path length, a transmission grating, and a 256 channel InGaAs detector that measured the above stated wavelength range with 5–6 nm (∼32 cm −1 ) resolution. The analyzer produced high signal-to-noise ratio (SNR) spectra of samples in 5 s. Twenty-two property correlation models were developed for diesel, gasoline, and jet fuels with root mean squared error of correlation – cross-validated values that compared favorably to corresponding ASTM reproducibility values. The standard deviations of predicted properties for repeat measurements at 4, 24, and 38℃ were often better than ASTM documented repeatability values. The analyzer and diesel property models were tested by measuring seven diesel samples at a local ASTM certification laboratory. The standard deviations between the analyzer determined values and the ASTM measured values for these samples were generally better than the model root mean squared error of correlation—cross-validated values for each property.