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Modeling RP-1 fuel advanced distillation data using comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry and partial least squares analysis

机译:使用全面的二维气相色谱结合飞行时间质谱和偏最小二乘分析对RP-1燃料高级蒸馏数据进行建模

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摘要

Recent efforts in predicting rocket propulsion (RP-1) fuel performance through modeling put greater emphasis on obtaining detailed and accurate fuel properties, as well as elucidating the relationships between fuel compositions and their properties. Herein, we study multidimensional chromatographic data obtained by comprehensive two-dimensional gas chromatography combined with time-of-flight mass spectrometry (GC x GC-TOFMS) to analyze RP-1 fuels. For GC x GC separations, RTX-Wax (polar stationary phase) and RTX-1 (non-polar stationary phase) columns were implemented for the primary and secondary dimensions, respectively, to separate the chemical compound classes (alkanes, cycloalkanes, aromatics, etc.), providing a significant level of chemical compositional information. The GC x GC-TOFMS data were analyzed using partial least squares regression (PLS) chemometric analysis to model and predict advanced distillation curve (ADC) data for ten RP-1 fuels that were previously analyzed using the ADC method. The PLS modeling provides insight into the chemical species that impact the ADC data. The PLS modeling correlates compositional information found in the GC x GC-TOFMS chromatograms of each RP-1 fuel, and their respective ADC, and allows prediction of the ADC for each RP-1 fuel with good precision and accuracy. The root-mean-square error of calibration (RMSEC) ranged from 0.1 to 0.5 A degrees C, and was typically below similar to 0.2 A degrees C, for the PLS calibration of the ADC modeling with GC x GC-TOFMS data, indicating a good fit of the model to the calibration data. Likewise, the predictive power of the overall method via PLS modeling was assessed using leave-one-out cross-validation (LOOCV) yielding root-mean-square error of cross-validation (RMSECV) ranging from 1.4 to 2.6 A degrees C, and was typically below similar to 2.0 A degrees C, at each % distilled measurement point during the ADC analysis.
机译:通过建模来预测火箭推进(RP-1)燃料性能的最新努力更加着重于获得详细而准确的燃料特性,以及阐明燃料成分与其特性之间的关系。本文中,我们研究了通过综合二维气相色谱结合飞行时间质谱(GC x GC-TOFMS)来分析RP-1燃料而获得的多维色谱数据。对于GC x GC分离,分别在一级和二级尺寸上采用RTX-Wax(极性固定相)和RTX-1(非极性固定相)色谱柱来分离化合物类别(烷烃,环烷烃,芳烃,等),以提供重要水平的化学成分信息。使用偏最小二乘回归(PLS)化学计量分析来分析GC x GC-TOFMS数据,以建模和预测先前使用ADC方法分析的十种RP-1燃料的高级蒸馏曲线(ADC)数据。 PLS建模可深入了解影响ADC数据的化学物质。 PLS建模将每种RP-1燃料及其各自的ADC的GC x GC-TOFMS色谱图中的组成信息相关联,并允许以良好的精度和准确性来预测每种RP-1燃料的ADC。对于使用GC x GC-TOFMS数据进行ADC建模的PLS校准,校准的均方根误差(RMSEC)为0.1至0.5 A摄氏度,并且通常低于0.2 A摄氏度。模型与校准数据的良好拟合。同样,使用留一法交叉验证(LOOCV)评估了整个方法通过PLS建模的预测能力,产生的交叉验证的均方根误差(RMSECV)为1.4至2.6 A摄氏度,并且在ADC分析过程中,每个蒸馏测量点的%都通常低于2.0 A摄氏度。

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