Multivariate prediction of eight kerosene properties employing vapour-phase mid-infrared spectrometry ☆

Abstract

Eight physico-chemical properties of kerosene (aviation jet fuel) are predicted employing vapour-phase generation, Fourier transform mid-infrared (FT-MIR) spectra and partial least squares regression (PLS). Two devices were implemented and studied in order to generate the kerosene vapour from 100 liquid samples from a Spanish refinery. One of them is very simple whilst the other one requires thermostatic and gas flow controls. The FT-MIR spectra are recorded and used to deploy PLS models for each property (distillation curve, flash point, freezing point, percentage of aromatics and viscosity) and each device. In general, the simplest device yields the more satisfactory models. Several criteria are used to evaluate their performance: the average prediction error (corrected to take into account the error in the reference values), the F-test to assess the absence of bias in the predictions, repeatability and reproducibility. In general, all the models provide unbiased predictions, with low average errors and good precision.