Estimation of Middle Distillate Fuel Properties by FT-IR and Chemometrics. Part I. Calibrations and Validations.

Fodor, G.; Hutzler, Scott A

doi:10.21236/ada333512

Cited by 2 publications

(6 citation statements)

References 11 publications

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“…Parameters such as total sulfur, cetane number, cetane index, pour point, cloud point, flash point, polyaromatic content, density, viscosity, total ash, and water content are some of the key parameters that determine the quality of diesel fuels. − The desired specifications of these properties of diesel fuel also vary region to region globally depending on climatic conditions and government regulations. − In refineries, also, a quick method of determining these properties of diesel fuels in blending various process streams can help to optimize the diesel product per its specifications. Conventional methods of analyses of individual properties, however, are laborious, time-consuming, uneconomical, and often not practical. − In this article, we propose an analytical methodology where a single compositional analysis can be used to predict these diesel properties by a composition-based statistical model. We demonstrate this capability of the statistical models by predicting cloud point, pour point, and cetane index for diesel samples.…”

Section: Introductionmentioning

confidence: 99%

Property Prediction of Diesel Fuel Based on the Composition Analysis Data by two-Dimensional Gas Chromatography

et al. 2018

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The objective of the present study is to develop robust statistical models for the prediction of critical diesel properties such as cloud point, pour point, and cetane index with composition inputs such as n-Paraffins, Iso-paraffins, Naphthenes, and Aromatics (PINA) obtained by flow modulated two-dimensional gas chromatography with flame ionization detection (GC×GC-FID). A single gas chromatographic measurement coupled with models to predict the key physical properties is attractive for refiners to make quick decisions in optimizing diesel blending. We present a partial least-squares (PLS) linear regression statistical model that has been developed using 41 data sets of diesel samples with different compositions, out of which 33 samples were used for the calibration and eight samples for validation of the model. The R 2 values obtained for cloud point, pour point, and cetane index were 0.92, 0.93, and 0.92 with standard deviations of 1.20, 1.50, and 0.40, respectively. The average relative errors for predicted values of cloud point, pour point, and cetane index are found to be 0.86, 1.02, and 0.25, respectively. The PINA analyses of diesel and kerosene samples were carried out using flow modulated GC×GC with flame ionization detection (FID). The technique adapts reverse phase gas chromatography with two capillary chromatographic columns; the columns differ in length, diameter, stationary phase, and film thickness to get maximum peak resolution. The gravimetric blends of high purity reference standards of paraffins, naphthenes, and aromatic compounds (PINA) with variable carbon numbers were used for identification and to draw the boundaries for group types. Monoaromatic and polyaromatic content obtained for diesel and kerosene samples by the flow modulated GC×GC method were comparable to the results obtained by the High Performance Liquid Chromatographic (HPLC) method as per IP 391 or ASTM D 6591. Repeatability and reproducibility of the GC×GC analysis were performed for several samples to validate the method. It has been found that the HPLC method for the determination of aromatics content using a single calibration standard for each type, such as mono-, di-, and polyaromatics, causes a small error in the quantification in some of the samples as the refractive indices of all the aromatic species present in the diesel and kerosene samples vary depending on the addition of alkyl side chains; the presence of heteroatoms such as sulfur, nitrogen, and oxygen; etc.

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Section: Introductionmentioning

confidence: 99%

Property Prediction of Diesel Fuel Based on the Composition Analysis Data by two-Dimensional Gas Chromatography

et al. 2018

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“…In addition, the data points in the difference plot will form a horizontal line at exactly zero % difference. A typical % difference plot will show data points distributed randomly above and below the zero line within a narrow range. − …”

Section: Resultsmentioning

confidence: 99%

“…These methods are reliable, accurate, and widely accepted but they also have some disadvantages. These methods require a large amount of sample, consume time, and may involve the use of toxic or environmentally dangerous reagents …”

Section: Introductionmentioning

confidence: 99%

“…It uses mathematical methods and computer science for the analysis of results to predict the physical and/or chemical properties of the analyzed samples. For analyzing chemical systems, including fuels, multivariate calibration has been used as one application of chemometrics. − …”

Section: Introductionmentioning

confidence: 99%

“…It produces well-defined peaks at wavelengths between 2.5 and 25 μm, corresponding to the 4000–650 cm –1 wavenumber region. Use of near-IR and FT-IR spectroscopy to determine diesel and gasoline fuel properties has been extensive. − ,, Furthermore, there are some FTIR and/or NIR applications where chemometrics was involved for diesel and diesel/biodiesel quality screening as well as detection of contaminations in gasoline and diesel. − ,− …”

Section: Introductionmentioning

confidence: 99%

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Chemometrics-Based Analytical Method Using FTIR Spectroscopic Data To Predict Diesel and Diesel/Diesel Blend Properties

2016

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In the hydrocarbons downstream business, it is very beneficial to quickly and reliably determine the physical and/or chemical properties of fuels. In this context, a nondestructive method was applied using midband Fourier transform infrared (FTIR) spectroscopy in association with multivariate partial least squares (PLS) chemometrics to determine the properties of nine groups of middle distillates (diesels) boiling in the range 180−370 °C. This method enables identification of one single diesel property at a time or a group of properties (32 properties) simultaneously in the spectral data between 4000−650 cm −1 ; with a minimum number of steps and without any sample preparation. The method was further used for two blends prepared from individual diesel samples. The results showed that using PLS models to process FTIR data is a practical analytical method to predict diesel fuel properties. Statistically, the results obtained showed low standard deviations, a very low root mean square error of cross-validation (RMSECV), low uncertainty values, less than 10 factors, but high correlation coefficient, R 2 , and performance index (PI) values.

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Estimation of Middle Distillate Fuel Properties by FT-IR and Chemometrics. Part I. Calibrations and Validations.

Cited by 2 publications

References 11 publications

Property Prediction of Diesel Fuel Based on the Composition Analysis Data by two-Dimensional Gas Chromatography

Property Prediction of Diesel Fuel Based on the Composition Analysis Data by two-Dimensional Gas Chromatography

Chemometrics-Based Analytical Method Using FTIR Spectroscopic Data To Predict Diesel and Diesel/Diesel Blend Properties

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