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A review of aviation turbine fuel chemical composition-property relations

机译：航空汽轮机燃料化学成分 - 财产关系综述

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The fuel certification process is a necessary step for all aviation fuels before deployment. This approval process is composed of multiple fuel physiochemical properties (e.g., density, viscosity, flash point, freezing point, etc.) testing. Measuring fuel properties is a very time consuming and cost-intensive process. Thus, having a tool capable of predicting fuel properties based on a small amount of fuel, utilizing fuel chemical composition, would be of essential value for an increased incentive towards a long-lasting renewable jet fuel production. The first step in achieving this is discovering the detailed chemical composition. The consecutive step is the development of predictive models that correlate the fuel chemical composition to the fuel physiochemical properties. This review provides an overview of correlation techniques and methods (from 1955 to 2019) organized according to four main categories: NMR spectroscopy (alone and in combination with chromatographic techniques), IR spectroscopy (NIR and FTIR), Raman spectroscopy, and gas chromatography (one-dimensional and two-dimensional). These techniques and their predictive capability were compared according to several criteria, specifically: the coefficient of determination (R-2) and the measure of the uncertainty of future predictions, which includes standard deviation of residuals, root-mean-square-error (RMSE), root-mean-square-error of prediction (RMSEP), standard error of prediction (SEP), root-mean-square-error of cross-validation (RMSECV), mean absolute error (MAE), and mean absolute percentage error (MAPE). In conclusion, this work summarizes an indepth review of all the accomplishments as well as the challenges required to be overcome in the field of accurately correlating chemistry to fuel properties.
机译：燃料认证过程是部署前所有航空燃料的必要步骤。该批准过程由多种燃料生理化学特性（例如，密度，粘度，闪点，冷冻点等）组成。测量燃料特性是一种非常耗时和成本密集的过程。因此，具有能够通过燃料化学组合物的少量燃料来预测燃料特性的工具，将是对长期可再生射流生产的激励增加的基本价值。实现这一点的第一步是发现详细的化学成分。连续步骤是开发预测模型，其将燃料化学组合物与燃料生理学性质相关联。本综述概述了关联技术和方法（从1955年到2019年）根据四个主要类别组织：NMR光谱（单独和与色谱技术组合），IR光谱（NIR和FTIR），拉曼光谱和气相色谱（一维和二维）。这些技术和它们的预测能力是根据若干标准进行比较，具体而言，具体实施方式：确定系数（R-2）和减少未来预测的不确定度，包括残差的标准偏差，根均值 - 误差（RMSE ），预测（RMSEP）的根均方误差，预测标准误差（SEP），横向验证（RMSECV）的根均方误差（RMSECV），平均值误差（MAE），以及平均绝对百分比误差（mape）。总之，这项工作总结了对所有成就的印度审查以及准确地将化学技术克服所需的挑战，以燃料特性。

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