Characterisation of middle-distillates by comprehensive two-dimensional gas chromatography (GC×GC): A powerful alternative for performing various standard analysis of middle-distillates

Chemometric Analysis Combined with GC × GC-FID and ESI HR-MS to Evaluate Ultralow-Sulfur Diesel Stability

Diesel has been the most employed fuel in highway and nonhighway transportation systems. Many studies over the past years have attempted to classify diesel as a stable or unstable composition since this fuel can still degrade during storage or thermal oxidative processes. Products generated because of such degradation are the reason for the formation of soluble gums and insoluble organic particulates, which in turn cause a negative influence on engine performance. This work reports a detailed composition of nonpolar and polar compounds in many ultralow-sulfur diesel (ULSD) samples by comprehensive two-dimensional gas chromatography with a flame ionization detector (GC × GC-FID) and electrospray ionization high-resolution mass spectrometry (ESI HR-MS). In addition, chemometric approaches were applied for ULSD storage stability investigation. GC × GC-FID experiments achieved the nonpolar chemical characterization for the ULSD samples, including all main hydrocarbon classes: paraffins, mono- and dinaphthenics and olefins, and aromatics. The GC × GC-FID data combined with principal component analysis (PCA) described that the separation of the samples' concerning storage stability was mainly due to the contents of mono- and diaromatic compounds in the unstable ULSD samples. Moreover, PCA was also applied to the ESI (±) data set, and the results highlight the presence of compounds belonging to O class (natural antioxidants), which decrease the rate of oxygen consumption in the fuel, characterizing it as stable composition. The basic nitrogen compounds are mostly present in the stable ULSD samples indicating that they did not affect the stability of the fuel. On the other hand, the HC classes presented pronounced abundance among unstable ULSD samples suggesting that the fuel degradation may go through the oxidation of hydrocarbons and the formation of Ox compounds as byproducts. Furthermore, MS/MS experiments point to the formation of CcHhNnOo-like precursor species, which can react with each other and lead to the formation of gums and insoluble sediments in the fuel. In summary, the results express the potential of using the GC × GC-FID and ESI (±) Orbitrap MS techniques as valuable tools for diesel stability evaluations.

Oil species identification based on fluorescence excitation-emission matrix and transformer-based deep learning

After oil spills are found at sea, the identification on oil species can help determine the source of leakage and form the plan of post-accident treatment. Since the fluorometric characteristics of petroleum hydrocarbon reflect its molecular structure, the composition of oil spills could potentially be inferred using the fluorescence spectroscopy method. The excitation-emission matrix (EEM) includes additional fluorescence information in the dimension of excitation wavelength, which could be useful to identify oil species. This study proposed an oil species identification model using transformer network. The EEMs of oil pollutants are reconstructed into sequenced patch input that consists of the fluorometric spectra obtained under the different excitation wavelengths. The comparative experiments show that the proposed model can reduce the incorrect predictions and achieve higher identification accuracies than the regular convolutional neural networks that have been used in the previous studies. According to the structure of transformer network, an ablation experiment is also designed to evaluate the contributions of different input patches and seek for the optimal excitation wavelengths for oil species identification. The proposed model is expected to identify oil species, and even other fluorescent materials, based on the fluorometric spectra collected under multiple excitation wavelengths.

Experimental Analysis on the Optimal Excitation Wavelength for Fine-Grained Identification of Refined Oil Pollutants on Water Surface Based on Laser-Induced Fluorescence

Laser-induced fluorescence (LIF) is an effective and all-weather oil spill identification method that has been widely applied for oil spill monitoring. However, the distinguishability on oil types was seldom considered while selecting the excitation wavelengths. This study is intended to find the optimal excitation wavelength for fine-grained classification of refined oil pollutants using LIF by comparing the distinguishability of fluorometric spectra under various excitation wavelengths on some typical types of refined oil samples. The results show that the fluorometric spectra of oil samples significantly vary under different excitation wavelengths, and the four types of oil applied in this study are most likely to be distinguished under the excitation wavelengths of 395 nm and 420 nm. This study is expected to improve the ability of oil types identification using LIF method without increasing time or other cost, and also provide theoretical basis for the development of portable LIF devices for oil spill types identification.

Multivariate statistics for summarizing diesel feeds for flammability attributes using comprehensive two-dimensional gas chromatography

Comprehensive 2D gas chromatography has been utilized for analyzing complex mixtures of hydrocarbons of diesel feeds. Here, we evaluated 19 diesel feeds for their paraffinic, naphthenic, and aromatic group compositions dictating their flammability properties. Compositional ranges of feeds were as follows: paraffins: 9.6-57.8%, naphthenes: 7.9-38.5%, and aromatics: 10.5-82.3%. Diesel's flammability performance is estimated by thermodynamic conditions and rates of radical formation of hydrocarbon type in actual engine condition, limiting cetane number. However, limitations are overcome by understanding the relative compositional variations of feeds by simple ranking of feeds based on C15-16 compositions. Due to the multidimensional variability of feeds, a principal component analysis was adopted later for its distinguishing capability. Paraffinic, naphthenic, and aromatic group's principal component analysis clustered up feeds based on the higher concentration of individual hydrocarbon group. We explored hierarchical cluster analysis to organize feeds into classes of mixed C9 to C26 paraffin's composition in the diesel range. Further, for discriminating C15-C16 enriched and depleted feeds in total paraffin composition, a row dendrogram with heat map was drawn. The above multivariate methods have led to a fair distinction of nonadditive feed compositions influencing flammability properties by radical formation rate.

Forensic Investigations of Diesel Oil Spills in the Environment Using Comprehensive Two-Dimensional Gas Chromatography-High Resolution Mass Spectrometry and Chemometrics: New Perspectives in the Absence of Recalcitrant Biomarkers

Forensic investigations of oil spills aim to find the responsible source(s) of the spill. Oil weathering processes change the chemical composition of the spilled oil and make the matching of oil spill samples to potential sources difficult. Diesel oil spill cases are more challenging, because biomarkers recalcitrant to long-term weathering are absent. We developed and tested a new method for the analysis and matching of diesel oil spills using two-dimensional gas chromatography-high resolution mass spectrometry (GC × GC - HRMS) and 2D-CHEMSIC (2-Dimensional CHEMometric analysis of Selected Ion Chromatograms), an extension of the CHEMSIC method to GC × GC data. The 2D-CHEMSIC performs pixel-based analysis using chemometrics on concatenated sections of 2D extracted ion chromatograms to assess the overall chemical variability of the samples, with potential applications for matching spill-source pairs in forensic investigations. The method was tested on samples from a number of diesel oil spill cases, (i) distinguishing chemically similar source diesels, (ii) investigating weathering effects on spill samples to determine type and degree of weathering, and (iii) improving the matching of diesel oil spills affected by weathering. Positive matches for spill-source pairs were identified after excluding the signals from the hydrocarbons most susceptible to evaporation, and photo-oxidized spills were also matched due to the presence of unaffected hydrocarbons. Forensic diagnostics obtained by the 2D-CHEMSIC were validated by the conventional CEN-Tr method.

Bioremediation of oil-based drill cuttings by a halophilic consortium isolated from oil-contaminated saline soil

Oil-based drill cuttings are hazardous wastes containing complex hydrocarbons, heavy metals, and brine. Their remediation is a crucial step before release to the environment. In this work, we enriched a halophilic consortium, from oil-polluted saline soil, which is capable of degrading diesel as the main pollutant of oil-based drill cuttings. The degradation ability of the consortium was evaluated in microcosms using two different diluting agents (fine sand and biologically active soil). During the bioremediation process, the bacterial community dynamics of the microcosms was surveyed using PCR amplification of a fragment of 16S rRNA gene followed by denaturing gradient gel electrophoresis (DGGE). The diesel degradation rates were monitored by total petroleum hydrocarbon (TPH) measurement and the total count of heterotrophic and diesel-degrading bacteria. After 3 months, the microcosm containing fine sand and drill cuttings with the ratio of 1:1 (initial TPH of 36,000 mg/kg) showed the highest TPH removal (40%) and its dominant bacterial isolates belonged to the genera Dietzia, Arthrobacter, and Halomonas. DGGE results also confirmed the role of these genera in drill cuttings remediation. DGGE analysis of the bacterial diversity showed that Propionibacterium, Salinimicrobium, Marinobacter, and Dietzia are dominant in active soil microcosm; whereas Bacillus, Salinibacillus, and Marinobacter are abundant in sand microcosm. Our results suggest that the bioaugmentation strategy would be more successful if the diluting agent does not contain a complex microbial community.

On-line Analysis of Nitrogen Containing Compounds in Complex Hydrocarbon Matrixes

The shift to heavy crude oils and the use of alternative fossil resources such as shale oil are a challenge for the petrochemical industry. The composition of heavy crude oils and shale oils varies substantially depending on the origin of the mixture. In particular they contain an increased amount of nitrogen containing compounds compared to the conventionally used sweet crude oils. As nitrogen compounds have an influence on the operation of thermal processes occurring in coker units and steam crackers, and as some species are considered as environmentally hazardous, a detailed analysis of the reactions involving nitrogen containing compounds under pyrolysis conditions provides valuable information. Therefore a novel method has been developed and validated with a feedstock containing a high nitrogen content, i.e., a shale oil. First, the feed was characterized offline by comprehensive two-dimensional gas chromatography (GC × GC) coupled with a nitrogen chemiluminescence detector (NCD). In a second step the on-line analysis method was developed and tested on a steam cracking pilot plant by feeding pyridine dissolved in heptane. The former being a representative compound for one of the most abundant classes of compounds present in shale oil. The composition of the reactor effluent was determined via an in-house developed automated sampling system followed by immediate injection of the sample on a GC × GC coupled with a time-of-flight mass spectrometer (TOF-MS), flame ionization detector (FID) and NCD. A novel method for quantitative analysis of nitrogen containing compounds using NCD and 2-chloropyridine as an internal standard has been developed and demonstrated.

Recent advances in the application of 2-dimensional gas chromatography with soft and hard ionisation time-of-flight mass spectrometry in environmental analysis

Two-dimensional gas chromatography has huge power for separating complex mixtures. The principles of the technique are outlined together with an overview of detection methods applicable to GC × GC column effluent with a focus on selectivity. Applications of GC × GC techniques in the analysis of petroleum-related and airborne particulate matter samples are reviewed. Mass spectrometric detection can be used alongside spectral libraries to identify eluted compounds, but in complex petroleum-related and atmospheric samples, when used conventionally at high ionisation energies, may not allow differentiation of structural isomers. Available low energy ionisation methods are reviewed and an example given of the additional structural information which can be extracted by measuring mass spectra at both low and high ionisation energies, hence greatly enhancing the selectivity of the technique.

Highly selective condensation of biomass-derived methyl ketones as a source of aviation fuel

Aviation fuel (i.e., jet fuel) requires a mixture of C9 -C16 hydrocarbons having both a high energy density and a low freezing point. While jet fuel is currently produced from petroleum, increasing concern with the release of CO2 into the atmosphere from the combustion of petroleum-based fuels has led to policy changes mandating the inclusion of biomass-based fuels into the fuel pool. Here we report a novel way to produce a mixture of branched cyclohexane derivatives in very high yield (>94 %) that match or exceed many required properties of jet fuel. As starting materials, we use a mixture of n-alkyl methyl ketones and their derivatives obtained from biomass. These synthons are condensed into trimers via base-catalyzed aldol condensation and Michael addition. Hydrodeoxygenation of these products yields mixtures of C12 -C21 branched, cyclic alkanes. Using models for predicting the carbon number distribution obtained from a mixture of n-alkyl methyl ketones and for predicting the boiling point distribution of the final mixture of cyclic alkanes, we show that it is possible to define the mixture of synthons that will closely reproduce the distillation curve of traditional jet fuel.

The discriminant pixel approach: a new tool for the rational interpretation of GCxGC-MS chromatograms

GCxGC is now recognized as the most suited analytical technique for the characterization of complex mixtures of volatile compounds; it is implemented worldwide in academic and industrial laboratories. However, in the frame of comprehensive analysis of non-target analytes, going beyond the visual examination of the color plots remains challenging for most users. We propose a strategy that aims at classifying chromatograms according to the chemical composition of the samples while determining the origin of the discrimination between different classes of samples: the discriminant pixel approach. After data pre-processing and time-alignment, the discriminatory power of each chromatogram pixel for a given class was defined as its correlation with the membership to this class. Using a peak finding algorithm, the most discriminant pixels were then linked to chromatographic peaks. Finally, crosschecking with mass spectrometry data enabled to establish relationships with compounds that could consequently be considered as candidate class markers. This strategy was applied to a large experimental data set of 145 GCxGC-MS chromatograms of tobacco extracts corresponding to three distinct classes of tobacco.

Combination of dynamic time warping and multivariate analysis for the comparison of comprehensive two-dimensional gas chromatograms: application to plant extracts

Comprehensive two-dimensional gas chromatography (GC x GC) is now recognized as the preferred technique for the detailed analysis and characterization of complex mixtures of volatile compounds. However, for comparison purposes, taking into account all the information contained in the chromatogram is far from trivial. In this paper, it is shown that the combination of peak alignment by dynamic time warping and multivariate analysis facilitated the comparison of complex chromatograms of tobacco extracts. The comparison is shown to be efficient enough to provide a clear discrimination among three types of tobacco. A tentative interpretation of loadings is presented in order to give access to the compounds which differ from one sample to another. Once located, mass spectrometry was used to identify markers of tobacco type.