Twenty-three gas oil samples from different origins were analyzed in positive and negative ion modes by electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry (ESI(±)-FT-ICR MS). Sample ionization and ion transfer conditions were first optimized using Design of Experiment approach. Advanced characterization of basic and neutral nitrogen compounds in these samples was then performed through ESI(±)-FT-ICR MS analysis. A good repeatability was observed from the analysis of six replicates for each gas oil sample. Significant differences in molecular composition were spotted between the gas oils, either considering identified heteroatomic classes or within nitrogen families and were later correlated to samples macroscopic properties. The evolution of nitrogen relative intensities for one feed and two corresponding effluents has also been studied to monitor hydrotreatment reaction pathways toward aromaticity and alkylation levels evolutions.

Sulfur content in gas oils is strictly regulated by legal specifications for environmental reasons. Gas oils are composed of various aromatic sulfur compounds, and some of them are known to be very refractory for sulfur removal reactions. Thus, an accurate analysis of sulfur compounds is important to find the appropriate operating conditions of the gas oil hydrotreating processes. Aromatic sulfur compounds contained in 23 gas oils samples were analyzed using APPI(+)-FT-ICR MS considering six replicates. Significant differences were spotted within several processed gas oils. A comparison of one feed and its corresponding effluents also confirmed the well-known refractory character of sulfur compounds such as polyalkylated dibenzothiophenes. To go deeper in the molecular exploration, chemometric tools were applied on this spectral data set including principal component analysis (PCA) and hierarchical cluster analysis (HCA). A unique data rearrangement was performed directly inspired on DBE vs carbon number plots that are systematically used in petroleomics studies. Then, these chemometric tools provided a successful classification of each type of gas oils. The PCA model has also been validated on mixed blends allowing us to conclude that it could be applied to unknown samples in order to identify the process used to produce them. Moreover, the exploration of the generated loadings revealed key types of molecules driving the classification such as C3-DBT which is a dibenzothiophene core with three additional carbon atoms. Indeed, it is known to remain mainly in deeply hydrotreated samples, validating previous observations regarding its potential refractory character. The ability of chemometric tools to extract specific molecular information from ultra-high resolution MS spectra reveals its huge potential for an exhaustive study of highly complex mixtures such as crude oils.

A total of 18 vacuum gas oils have been analyzed by Fourier transform ion cyclotron resonance mass spectrometry considering six replicates in three different ionization modes (electrospray ionization (ESI)(+), ESI(−), and atmospheric pressure photoionization (APPI)(+)) to characterize the nitrogen and sulfur compounds contained in these samples. Classical data analysis has been first performed on generated data sets using double bond equivalents (DBE) versus number of carbon atoms (#C) plots in order to observe similarities and differences within the nitrogen and sulfur-containing molecular classes from samples produced by different industrial processes. In a second step, three-way arrays have been generated for each ionization mode considering three dimensions: DBE related to aromaticity, number of carbon atoms related to alkylation, and sample. These threeway arrays have then be concatenated using low-level data fusion strategy to obtain a new tensor with three new modes: aromaticity, alkylation, and sample. The PARAFAC method has then been applied for the first time to this three-way data structure. A two components decomposition has allowed us to highlight unique samples with unexpected reactivity behaviors throughout hydrotreatment. The obtained loadings led to the identification of the variables responsible for this specific character. This original strategy has provided a fast visualization tool able to highlight simultaneously the impact of the three ionization modes in order to explain the differences between the samples and compare them.

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