Rapid Fuel Quality Surveillance through Chemometric Modeling of Near-Infrared Spectra

Morris, Robert E.; Hammond, Mark; Cramer, Jeffrey A.; Johnson, Kevin; Giordano, Braden C.; Kramer, Kirsten; Rose-Pehrsson, Susan L.

doi:10.1021/ef800869t

Cited by 52 publications

(35 citation statements)

References 46 publications

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“…Efforts have been made to address the control problem, but the in-line instrumentation performance has seldom been compared to the off-line standard test ones for the sake of product certification (using repeatability and reprodutibility); correlation measures are provided instead [4,13,14]. In this sense, data validation is the key to assure the measurement precision, enhancing the signals of the sensors.…”

Section: The Ilbc Unit Operationmentioning

confidence: 99%

“…Another important question refers to the analyzer sensitivity to a particular property. Such a question has been extensively studied in the literature, for example, using NIR technology [13], and different commercial solutions addressing this issue have been released in the market, for example, the FUELex analyzer of the Bruker Optics company. In fact, the search for surrogated variables and correlation development are encouraged.…”

Section: The Ilbc Unit Operationmentioning

confidence: 99%

“…These analyzers are usually cheap, reliable, and fast, making them appropriate for composing an ILBC scheme. For properties hard to measure in line, inferences based on combinations of electromagnetic spectroscopy and inference models have become very popular both in academic and industrial applications [13]. The combination of NIR spectrometers and multivariate calibration techniques certainly constitutes the most popular alternative for inference development due to fast response times and high precision.…”

Section: The Ilbc Unit Operationmentioning

confidence: 99%

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Rethinking Petroleum Products Certification

Feital

Lima

Pinto

et al. 2013

Journal of Petroleum Engineering

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Facing various challenges in the everchanging refining landscape, it is essential that refiners raise their operations to new levels of performance. Advances in in-line blending (ILB) technology accuracy and reliability have encouraged refiners to take a step forward. Having ILB as a precursor, a new methodology is in concern: the so-called in-line certification (ILC) procedure. Blending processes make use of in-line measurements which, at least in principle, can be used to certificate the product, if the precision and accuracy of available in-line measurements are comparable to measurements provided by standard off-line tests. Such procedure may allow for significant reduction in refinery's tank farming and product inventory, increase of process flexibility, and reliability with benefits to company image. The main limitations for real-world ILC applications in the oil industry remain at the legal and technological levels. This paper proposes novel concepts and foundations of a basic in-line certification model for petroleum products regarding current interdisciplinary challenges and promising solutions.

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Section: The Ilbc Unit Operationmentioning

confidence: 99%

Section: The Ilbc Unit Operationmentioning

confidence: 99%

Section: The Ilbc Unit Operationmentioning

confidence: 99%

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Rethinking Petroleum Products Certification

Feital

Lima

Pinto

et al. 2013

Journal of Petroleum Engineering

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“…Chemometric tools to classify or analyze biodiesels for chemical information have included principal component analysis (PCA) [17,18], PCA-projections to latent structures [14], partial least squares (PLS) [19,20], PLS combined with PCA, hierarchical cluster analysis (HCA), Knearest neighbor (KNN) [21], and support vector machines (SVM) with PLS/ multivariate adaptive regression splines (MARS) [22]. Other applications of chemometric methods include quantitative determinations of blends with biodiesel, petrodiesel, and a renewable hydrocarbon by NIR spectrometry [4,23], the percent composition in biodiesel/petrodiesel by GC×GC-TOFMS [11], evaluation of blend quality by proton NMR [24], classification of blends by FT-ICR 6 MS [22], and the determination of methyl esters in biodiesel blends using FTIR-ATR and FTNIR [25]. Chemometric tools have been applied successfully with complex data sets from GC-DMS analyses although matrix interferences were not considered significant complications [15].…”

Section: Introductionmentioning

confidence: 99%

Classification of biodiesel and fuel blends using gas chromatography – differential mobility spectrometry with cluster analysis and isolation of C18:3 me by dual ion filtering

Pasupuleti

Eiceman

Pierce

2016

Talanta

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Fatty acid alkyl esters (FAAEs) were determined at 10-100mg/L in biodiesel and blends with petrodiesel without sample pre-treatment using gas chromatography with a tandem differential mobility detector. Selectivity was provided through chromatographic separations and atmospheric pressure chemical ionization reactions in the detector with mobility characterization of gas ions. Limits of detection were ~0.5ng with an average of 2.98% RSD for peak area precision, ≤1.3% RSD for retention time precision, and ≤9.2% RSD for compensation voltage precision. Biodiesel blends were classified using principal component analysis (PCA) and hierarchical cluster analysis (HCA). Unsupervised cluster analysis captured 52.72% of variance in a single PC while supervised analysis captured 71.64% of variance using Fisher ratio feature selection. Test set predictions showed successful clustering according to source or feedstock when regressed onto the training set model. Detection of the regulated substance methyl linolenate (C18:3 me) was achieved in 6-10s with a 1m long capillary column using dual ion filtering in the tandem differential mobility detector.

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“…Because of the correlation of IR overtone bands with the chemical and physical properties of fuels, near-IR (NIR) spectroscopy has gained widespread acceptance as a secondary analytical method in fuel analysis [1][2][3][4][5][6][7][8][9][10][11][12][13][14]. Typically, using the partial least squares (PLS) method, NIR spectra of fuels are correlated with their known American Society for Testing and Materials (ASTM)-determined properties.…”

Section: Introductionmentioning

confidence: 99%

Calibration transfer of near‐IR partial least squares property models of fuels using virtual standards

Cooper

Larkin

Abdelkader

2011

Journal of Chemometrics

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a Partial least squares (PLS) models of 10 important jet and diesel fuel properties were built using spectra from a master near-IR dispersive instrument and then subsequently transferred to a secondary dispersive instrument via a novel calibration transfer method using virtual standards and a slope-bias correction. Implementation of the transfer requires that only seven spectra of neat solvents be acquired on the master and secondary instruments. The spectra of the neat solvents are then used to digitally replicate spectra from the calibration set to generate virtual standards. Comparison of PLS predictions for the master and secondary instrument virtual standards provides a simple but effective slope-bias correction for transfer. The transferred fuel properties include American Petroleum Institute gravity, % aromatics, cetane index, flashpoint, hydrogen content, % saturates, and distillation temperatures at 10%, 20%, 50%, and 90% volume recovered. Transfer error was lower than using either the pure solvents with a slope-bias correction or than using a piecewise direct standardization calibration transfer using fuel spectra. Transfer error was higher than when using actual fuels to transfer the calibration. The use of virtual standards eliminates the need to maintain either complex fuel standards or the master instrument for future instrument calibration transfers.

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Rapid Fuel Quality Surveillance through Chemometric Modeling of Near-Infrared Spectra

Cited by 52 publications

References 46 publications

Rethinking Petroleum Products Certification

Rethinking Petroleum Products Certification

Classification of biodiesel and fuel blends using gas chromatography – differential mobility spectrometry with cluster analysis and isolation of C18:3 me by dual ion filtering

Calibration transfer of near‐IR partial least squares property models of fuels using virtual standards

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