Hradecká Ivana, Vráblík Aleš, Frątczak Jakub, Sharkov Nikita, Černý Radek, Hönig Vladimír
ORLEN UniCRE a.s., Revoluční 1521/84, 400 01Ústí nad Labem, Czech Republic.
Department of Chemistry, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Kamýcká 129, 165 00Prague, Czech Republic.
ACS Omega. 2023 Jan 19;8(4):4038-4045. doi: 10.1021/acsomega.2c06845. eCollection 2023 Jan 31.
Diesel and biodiesel blends requires additives to improve fuel quality properties and engine performance. Diesel improvers are added before, during and/or after the fuel is blended. However, no accurate rapid and non-destructive analytical method is used during the fuel production that could determine the exact concentration of various types of improvers in diesel fuel. Thus, the aim of this study was to determine the concentration of several improvers in diesel matrices at the same time. Three types of diesel improvers, i.e., a cold-flow improver (CFI), a conductivity-lubricity improver (CLI), and a cetane number improver (CNI), were simultaneously determined by near-infrared (NIR) spectroscopy combined with multivariate statistical analysis and the partial least squares algorithm. The prediction models yielded high correlation coefficients ( ) >0.99 and satisfactory values of the root mean square error of calibration as follows: CLI 4.2 (mg·kg), CFI 4.6 (mg·kg), and CNI 5.3 (mg·kg). The residual standard deviation of the repeatability was calculated to be around 8%. These results highlight the potential of NIR spectroscopy for use as a fast, low-cost, and efficient tool to determine the concentrations of diesel improvers. Moreover, this technique is suitable for application during refinery production, especially for the purpose of online monitoring to prevent overdoses of additives and save financial expenses.
柴油和生物柴油混合物需要添加剂来改善燃料质量特性和发动机性能。柴油改进剂在燃料混合之前、期间和/或之后添加。然而,在燃料生产过程中没有使用准确、快速且无损的分析方法来确定柴油燃料中各种类型改进剂的确切浓度。因此,本研究的目的是同时测定柴油基质中几种改进剂的浓度。通过近红外(NIR)光谱结合多元统计分析和偏最小二乘法算法,同时测定了三种类型的柴油改进剂,即低温流动改进剂(CFI)、导电润滑改进剂(CLI)和十六烷值改进剂(CNI)。预测模型产生了高相关系数()>0.99,校准均方根误差的满意值如下:CLI为4.2(mg·kg),CFI为4.6(mg·kg),CNI为5.3(mg·kg)。重复性的残余标准偏差计算约为8%。这些结果突出了近红外光谱作为一种快速、低成本且高效的工具来测定柴油改进剂浓度的潜力。此外,该技术适用于炼油生产过程中的应用,特别是用于在线监测以防止添加剂过量并节省财务费用。