Chemical Composition and Low-Temperature Fluidity Properties of Jet Fuels

• Published in: Processes Vol. 9; no. 7; p. 1184
• Main Authors: Benavides, Alirio, Benjumea, Pedro, Cortés, Farid B., Ruiz, Marco A.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2021
• Subjects: Atomic properties; Aviation fuel; Carbon; Chemical composition; Chromatography; Composition effects; Crystallization; Fluidity; Freezing; Fuels; Gas chromatography; Gas turbine engines; Hydrocarbons; Jet engine fuels; Kerosene; Low temperature; Mass spectrometry; Mass spectroscopy; Melting points; Molecular structure; Naphthalene; Physicochemical properties; Property values; Refineries; Scientific imaging; Statistical analysis; Tetralin; Viscosity
• ISSN: 2227-9717; 2227-9717
• DOI: 10.3390/pr9071184

The physicochemical properties of petroleum-derived jet fuels mainly depend on their chemical composition, which can vary from sample to sample as a result of the diversity of the crude diet processed by the refinery. Jet fuels are exposed to very low temperatures both at altitude and on the ground in places subject to extreme climates and must be able to maintain their fluidity at these low temperatures otherwise the flow of fuel to turbine engines will be reduced or even stopped. In this work, an experimental evaluation of the effect of chemical composition on low-temperature fluidity properties of jet fuels (freezing point, crystallization onset temperature and viscosity at −20 °C) was carried out. Initially, a methodology based on gas chromatography coupled to mass spectrometry (GC–MS) was adapted to determine the composition of 70 samples of Jet A1 and Jet A fuels. This methodology allowed quantifying the content, in weight percentage, of five main families of hydrocarbons: paraffinic, naphthenic, aromatic, naphthalene derivatives, and tetralin- and indane-derived compounds. Fuel components were also grouped into 11 classes depending on structural characteristics and the number of carbon atoms in the compound. The latter compositional approach allowed obtaining more precise model regressions for predicting the composition–property dependence and identifying individual components or hydrocarbon classes contributing to increased or decreased property values.