Multivariate prediction of eight kerosene properties employing vapour-phase mid-infrared spectrometry☆

Gómez-Carracedo, M.P.; Calvino, M.; Fernández, Enrique Vega; Prada, D.; Muniategui-Lorenzo, Soledad

doi:10.1016/s0016-2361(02)00381-2

Cited by 25 publications

(12 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When hydrocarbon is oxidized with high temperature, different types of combustion reactions i.e. complete and incomplete combustion takes place [16]. Incomplete combustion is occurs when there is not enough oxygen to allow the fuel to react completely to produce carbon dioxide and water.…”

Section: Preparation Of Carbon Nanoparticlesmentioning

confidence: 99%

“…It also happens when the combustion is quenched by a heat sink such as a solid surface or flame trap. The stoichiometric chemical equation for incomplete combustion of hydrocarbon in oxygen is as follows [16]: Complete combustion is almost impossible to achieve. In reality, as actual combustion reactions come to equilibrium, a wide variety of major and minor species will be present such as carbon monoxide and pure carbon.…”

Section: Preparation Of Carbon Nanoparticlesmentioning

confidence: 99%

See 1 more Smart Citation

Synthesis of Carbon Nanoparticles from Kerosene and their Characterization by SEM⁄EDX, XRD and FTIR

Hossain¹,

Islam²

2013

NANO

View full text Add to dashboard Cite

Carbon nanoparticles (CNPs) were synthesized by a simple way of incomplete combustion of kerosene. Synthesized nanoparticles were characterized by Scanning electron microscope (SEM), Energy dispersive x-ray (EDX), Powder x-ray diffractometry (XRD) and Fourier transform infrared spectroscopy (FTIR). The SEM particle sizes of prepared CNPs were found to be non-uniform. The average size of particles is in the range of 20-100 nm. EDX analysis predicts the presence of pure carbon without any contamination. X-ray powder diffractometric analysis of prepared CNPs indicates the presence of large amounts of amorphous carbon material in association with hexagonal graphite lattice. FTIR spectroscopic analysis shows that the prepared CNPs are a mixture of elemental carbon and a trace amount of hydrocarbons.

show abstract

Section: Preparation Of Carbon Nanoparticlesmentioning

confidence: 99%

Section: Preparation Of Carbon Nanoparticlesmentioning

confidence: 99%

Synthesis of Carbon Nanoparticles from Kerosene and their Characterization by SEM⁄EDX, XRD and FTIR

Hossain¹,

Islam²

2013

NANO

View full text Add to dashboard Cite

show abstract

“…Jet fuels are kerosene-based mixtures of thousands of aromatic and aliphatic hydrocarbons that have been generally determined upon the operational requirements. 1,2 Fuel properties may change over time as the fuel ages or if the fuel is contaminated or mislabeled. Monitoring the changes of jet fuel physical properties is important because fuel used in highperformance aircrafts must meet rigorous specifications.…”

Section: Introductionmentioning

confidence: 99%

“…More applications of NIR can be found in review papers. 2,11,12 The absorption of NIR bands corresponds mainly to the overtones and combinations of fundamental stretching vibrations of C-H, O-H, and N-H. 13 NIR spectra allow physical properties that depend on the chemical compositions of jet fuels to be determined. 11 NIR spectroscopy and Raman spectroscopy in conjunction with multivariate calibration were used to determine certain properties quantitatively in the petrochemical industry, and the results indicated that the NIR data produced more statistically meaningful models than the Raman data.…”

Section: Introductionmentioning

confidence: 99%

Classification of Jet Fuel Properties by Near-Infrared Spectroscopy Using Fuzzy Rule-Building Expert Systems and Support Vector Machines

Bunker

Harrington

2010

Appl Spectrosc

View full text Add to dashboard Cite

Monitoring the changes of jet fuel physical properties is important because fuel used in high-performance aircraft must meet rigorous specifications. Near-infrared (NIR) spectroscopy is a fast method to characterize fuels. Because of the complexity of NIR spectral data, chemometric techniques are used to extract relevant information from spectral data to accurately classify physical properties of complex fuel samples. In this work, discrimination of fuel types and classification of flash point, freezing point, boiling point (10%, v/v), boiling point (50%, v/v), and boiling point (90%, v/v) of jet fuels (JP-5, JP-8, Jet A, and Jet A1) were investigated. Each physical property was divided into three classes, low, medium, and high ranges, using two evaluations with different class boundary definitions. The class boundaries function as the threshold to alarm when the fuel properties change. Optimal partial least squares discriminant analysis (oPLS-DA), fuzzy rule-building expert system (FuRES), and support vector machines (SVM) were used to build the calibration models between the NIR spectra and classes of physical property of jet fuels. OPLS-DA, FuRES, and SVM were compared with respect to prediction accuracy. The validation of the calibration model was conducted by applying bootstrap Latin partition (BLP), which gives a measure of precision. Prediction accuracy of 97 ± 2% of the flash point, 94 ± 2% of freezing point, 99 ± 1% of the boiling point (10%, v/v), 98 ± 2% of the boiling point (50%, v/v), and 96 ± 1% of the boiling point (90%, v/v) were obtained by FuRES in one boundaries definition. Both FuRES and SVM obtained statistically better prediction accuracy over those obtained by oPLS-DA. The results indicate that combined with chemometric classifiers NIR spectroscopy could be a fast method to monitor the changes of jet fuel physical properties.

show abstract

“…The application of quantitative chemometrics methods, particularly partial least squares (PLS) for multivariate chemical data, has been widespread in the petroleum laboratories [8,9]. Mid-infrared spectroscopy (FT-MIR) combined with PLS regression method have been extensively used in the analysis of physical and chemical properties of petrochemical products such as: gasoline, naphtha [8][9][10][11][12][13][14][15][16], kerosene, jet fuel [17][18][19][20][21], diesel [22,23], lubricants and oils [24,25].…”

Section: Introductionmentioning

confidence: 99%

Predicting % of crystallinity in FCC catalysts by FT‐MIR and PLS

Dago¹,

Talavera

Fernández³

et al. 2008

Journal of Chemometrics

View full text Add to dashboard Cite

a This paper describes an analytical procedure for prediction of percent of crystallinity of fluidized catalytic cracking catalysts (FCC) using Fourier transform mid infrared spectroscopy (FT-MIR) and partial least-squares (PLS) multivariate calibration technique. In order to make a robust regression model, multiplicative scatter correction (MSC) and smoothed second derivative pre-processing methods were tested. Root mean squared error of prediction (RMSEP) of an independent test set was used to measure the performance of the models. The comparison shows that reasonable values of RMSEP and RMSECV were obtained for PLS-MSC model (RMSEP ¼ 0.8% and RMSECV ¼ 1.3%). The accuracy of the results obtained by the PLS-MSC regression model is in accordance with the uncertainty of the XRPD reference method. The developed method can be implemented in a refinery laboratory environment with ease.

show abstract

Multivariate prediction of eight kerosene properties employing vapour-phase mid-infrared spectrometry☆

Cited by 25 publications

References 25 publications

Synthesis of Carbon Nanoparticles from Kerosene and their Characterization by SEM⁄EDX, XRD and FTIR

Synthesis of Carbon Nanoparticles from Kerosene and their Characterization by SEM⁄EDX, XRD and FTIR

Classification of Jet Fuel Properties by Near-Infrared Spectroscopy Using Fuzzy Rule-Building Expert Systems and Support Vector Machines

Predicting % of crystallinity in FCC catalysts by FT‐MIR and PLS

Contact Info

Product

Resources

About