Prediction of HEFA content in jet fuel using FTIR and chemometric methods

[Display omitted] •Quantification of Hydroprocessed Esters and Fatty Acids (HEFA) content in HEFA/jet fuel blends.•Development of predictive models using FTIR and multivariate calibration.•Utilization of partial least-square regression.•Good predictive ability and robustness of the developed model....

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Bibliographic Details
Published in:Fuel (Guildford) 2019-01, Vol.236, p.1458-1464
Main Authors: Vrtiška, Dan, Vozka, Petr, Váchová, Veronika, Šimáček, Pavel, Kilaz, Gozdem
Format: Article
Language:English
Subjects:
Aircraft
Aviation fuel
Biodiesel fuels
Biofuels
Biojet
Blending
Calibration
Diesel fuels
Esters
Fatty acids
FTIR spectroscopy
Gas turbine engines
Gas turbines
HEFA
Jet engine fuels
Jet fuel
Mathematical models
Mixtures
Partial least square regression
Prediction models
Principal component analysis
Scaling
Spectrum analysis
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Summary:[Display omitted] •Quantification of Hydroprocessed Esters and Fatty Acids (HEFA) content in HEFA/jet fuel blends.•Development of predictive models using FTIR and multivariate calibration.•Utilization of partial least-square regression.•Good predictive ability and robustness of the developed model. Alternative aviation fuel blending components are becoming a common component in gas turbine engines fuels. One of the blending components is Hydroprocessed Esters and Fatty Acids (HEFA). Predictive models based on a chemometric processing of FTIR spectra were developed as a reliable method for the determination of HEFA in petroleum jet fuel/HEFA blends. The concentration of HEFA in blends with jet fuel ranged between 0 and 60 wt%. The sample set composed of calibration and validation subsets. Additionally, several pre-processing techniques were evaluated, such as mean centering, variance scaling, derivation, and smoothing. The effectiveness of these models was highly dependent on the model parameters (pre-processing techniques and latent variables). The most robust model was able to predict HEFA content in all samples with a good predictive ability (root mean square error of prediction of 0.7 wt%).
ISSN:0016-2361
1873-7153
DOI:10.1016/j.fuel.2018.09.102