Comprehensive multidimensional separations for the analysis of petroleum

As feedstocks transition from conventional oil to unconventional petroleum sources and biomass, it will be necessary to determine whether a particular fuel or fuel blend is suitable for use in engines. Certifying a fuel as safe for use is time-consuming and expensive and must be performed for each new fuel. In principle, suitability of a fuel should be completely determined by its chemical composition. This composition can be probed through use of detailed analytical techniques such as gas chromatography-mass spectroscopy (GC-MS). In traditional analysis, chromatograms would be used to determine the details of the composition. In the approach taken in this paper, the chromatogram is assumed to be entirely representative of the composition of a fuel, and is used directly as the input to an algorithm in order to develop a model that is predictive of a fuel's suitability. When a new fuel is proposed for service, its suitability for any application could then be ascertained by using this model to compare its chromatogram with those of the fuels already known to be suitable for that application. In this paper, we lay the mathematical and informatics groundwork for a predictive model of hydrocarbon properties. The objective of this work was to develop a reliable model for unsupervised classification of the hydrocarbons as a prelude to developing a predictive model of their engine-relevant physical and chemical properties. A set of hydrocarbons including biodiesel fuels, gasoline, highway and marine diesel fuels, and crude oils was collected and GC-MS profiles obtained. These profiles were then analyzed using multi-way principal components analysis (MPCA), principal factors analysis (PARAFAC), and a self-organizing map (SOM), which is a kind of artificial neural network. It was found that, while MPCA and PARAFAC were able to recover descriptive models of the fuels, their linear nature obscured some of the finer physical details due to the widely varying composition of the fuels. The SOM was able to find a descriptive classification model which has the potential for practical recognition and perhaps prediction of fuel properties.

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“…This effect has motivated the use of GCxGC-TOFMS for petroleum analysis [14-18]. GCxGC-TOFMS has its disadvantages, however.…”

Section: Methodsmentioning

confidence: 99%

Unsupervised classification of petroleum Certified Reference Materials and other fuels by chemometric analysis of gas chromatography-mass spectrometry data

Rocha

Schantz

Sheen

et al. 2017

Fuel

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“…In the field of oil industry, GC×GC gives an unprecedented level of information [6] thanks to the use of two complementary separations combining different selectivities. It is very useful in the understanding of catalytic reactions or in the design of refining process units [7,8].…”

Section: Introductionmentioning

confidence: 99%

BARCHAN: Blob Alignment for Robust CHromatographic ANalysis

Duval

Moreaud

Hénon

et al. 2017

Journal of Chromatography A

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Two dimensional gas chromatography (GC×GC) plays a central role into the elucidation of complex samples. The automation of the identification of peak areas is of prime interest to obtain a fast and repeatable analysis of chromatograms. To determine the concentration of compounds or pseudo-compounds, templates of blobs are defined and superimposed on a reference chromatogram. The templates then need to be modified when different chromatograms are recorded. In this study, we present a chromatogram and template alignment method based on peak registration called BARCHAN. Peaks are identified using a robust mathematical morphology tool. The alignment is performed by a probabilistic estimation of a rigid transformation along the first dimension, and a non-rigid transformation in the second dimension, taking into account noise, outliers and missing peaks in a fully automated way. Resulting aligned chromatograms and masks are presented on two datasets. The proposed algorithm proves to be fast and reliable. It significantly reduces the time to results for GC×GC analysis.

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“…GC is also ideal for the analysis of dissolved gases present in crude oil, as gases are liberated from the liquid phase in the hot inlet and analyzed on the column set by the user desires. There are several excellent reviews that describe recent advances in GC applied to crude oil and petroleum‐based samples . The present paper focuses on the analysis of dissolved hydrogen sulfide (H 2 S) in crude oil.…”

Section: Introductionmentioning

confidence: 99%

A multidimensional gas chromatography method for the analysis of hydrogen sulfide in crude oil and crude oil headspace

Heshka

Hager

2014

J of Separation Science

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Two-dimensional heart-cutting gas chromatography is used to analyze dissolved hydrogen sulfide in crude samples. Liquid samples are separated first on an HP-PONA column, and the light sulfur gases are heart-cut to a GasPro column, where hydrogen sulfide is separated from other light sulfur gases and detected with a sulfur chemiluminescence detector. Heart-cutting is accomplished with the use of a Deans switch. Backflushing the columns after hydrogen sulfide detection eliminates any problems caused by high-boiling hydrocarbons in the samples. Dissolved hydrogen sulfide is quantified in 14 crude oil samples, and the results are shown in this work. The method is also applicable to the analysis of headspace hydrogen sulfide over crude oil samples. Gas hydrogen sulfide measurements are compared to liquid hydrogen sulfide measurements for the same sample set. The chromatographic system design is discussed, and chromatograms of representative gas and liquid measurements are shown.

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Comprehensive multidimensional separations for the analysis of petroleum

Cited by 66 publications

References 185 publications

Unsupervised classification of petroleum Certified Reference Materials and other fuels by chemometric analysis of gas chromatography-mass spectrometry data

Unsupervised classification of petroleum Certified Reference Materials and other fuels by chemometric analysis of gas chromatography-mass spectrometry data

BARCHAN: Blob Alignment for Robust CHromatographic ANalysis

A multidimensional gas chromatography method for the analysis of hydrogen sulfide in crude oil and crude oil headspace

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