Using Compact Proton Nuclear Magnetic Resonance at 80 MHz and Vibrational Spectroscopies and Data Fusion for Research Octane Number and Gasoline Additive Determination

Abstract

Commercial fuels are characterized by parameters, such as research octane number and contents of additives, such as ethanol, ethyl-t-butyl ether, ethyl-tert-methyl ether, olefins, etc. For fast and easy parameter determination without the need for sample preparation, we used compact and benchtop near-infrared (NIR), proton nuclear magnetic resonance ( 1 H NMR) at 80 MHz, and two Raman spectrometers to predict selected relevant fuel parameters of 179 samples known from CFR motor and norm-compliant analyses. Repeatability and reproducibility criteria according to ASTM and ISO norms served as goodness of prediction measures. The prediction relied on partial least squares regression type 1 yielding one target parameter and type 2 yielding simultaneously n target values. While PLS-1 provided more accurate results, PLS-2 might be further applicable to RON and oxygenated additive content determination. Among the methods applied, benchtop Raman and 1 H NMR performed best. Low-, mid-, and high-level data fusion were applied to transform pretreated subspectra from up to three individual techniques to result in pseudo-spectra, combined score matrices, or decision models, which further improved the accuracy of the RON prediction. Best results for RON were obtained with mid-level fusion of NIR, NMR, and Raman data yielding 63% of the predicted values within reproducibility of 0.2 and up to 97% within repeatability of 0.7 RON.

Application of green analytical chemistry to a green chemistry process: Magnetic resonance and Raman spectroscopic process monitoring of continuous ethanolic fermentation

“…The implemented MultiPurposeSampler (Gerstel, Muelheim an der Ruhr, Germany) was equipped with a stainless‐steel flow cell, VT98 tray, fast wash station, and injection valve. Through this arrangement, samples were channeled through the flow cell and transferred to the NMR spectrometer and/or pipetted into high‐performance liquid chromatography (HPLC) vials (Legner, Friesen, Voigt, Horst, & Jaeger, ). For HPLC analysis, samples of 2 ml each were collected.…”

Selection of the Effective Characteristic Spectra Based on the Chemical Structure and Its Application in Rapid Analysis of Ethanol Content in Gasoline

“…However, this method has disadvantages, including long analysis times and the inability to meet the needs of on-site analysis and real-time detection due to bulky instruments that cannot be easily carried and moved around for analysis. To solve this problem, some rapid analytical methods for measuring ethanol content in gasoline have been developed, for example, Raman spectroscopy 7 , 8 and Near-infrared (NIR) spectroscopy. 9 , 10 NIR is a fast, non-destructive analytical method that consumes small amounts of reagents.…”

“…However, this method has disadvantages, including long analysis times and the inability to meet the needs of on-site analysis and real-time detection due to bulky instruments that cannot be easily carried and moved around for analysis. To solve this problem, some rapid analytical methods for measuring ethanol content in gasoline have been developed, for example, Raman spectroscopy , and Near-infrared (NIR) spectroscopy. , NIR is a fast, non-destructive analytical method that consumes small amounts of reagents. − NIR spectroscopy combined with multivariate statistical analytical methods, such as partial least squares or principal component analysis, has been widely used in rapid analysis of ethanol content in gasoline. Mabood et al, using partial least squares and principal component analysis, established a rapid analytical method to determine ethanol content in gasoline using NIR spectroscopy, achieving a low root mean square error of prediction (RMSEP).…”

Using Spectroscopy and Support Vector Regression to Predict Gasoline Characteristics: A Comparison of1H NMR and NIR

“…There are numerous investigations reporting the successful application of both technologies in predicting some physical–chemical properties of gasoline. Among the most recent studies, it is possible to find applications of portable spectrometers or novel chemometric approaches that reportedly give more accurate predictions than the most established ones. − However, few studies have been published on the comparison of the 1 H NMR spectroscopy and NIR vibrational spectroscopy as potential techniques for properties estimation in the petrochemical industry, − and none of them includes the simultaneous estimation of the amount of gasoline physical–chemical properties analyzed in this work. In the current study we aim at comparing the performance of multivariate regression models developed using either NIR or 1 H NMR spectral data to predict 13 different gasoline parameters.…”