New Data Processing Method for Heavy Oil Components Analyzed by Fourier Transform Ion Cyclotron Resonance Mass Spectrometry

Question

1. What is the significance of fouling in the crude distillation unit?

2. What is the new data processing method for heavy oil components analyzed by Fourier transform ion cyclotron resonance mass spectrometry?

3. How was the AR sample separated for comprehensive analysis?

4. What ionization method used for Sa fraction?

Accepted Answer

Fouling in the crude distillation unit (CDU) significantly impacts energy efficiency. The CDU consumes large amounts of energy, making it crucial to optimize its operation. Fouling of the heat exchanger surface reduces energy efficiency, necessitating various initiatives for its reduction. Understanding the general properties of feed and product oils, as well as the physical and chemical details of their components, is essential for optimizing refinery operating conditions. Oils with higher boiling points have increasingly complex compositions, requiring advanced techniques like ultra-high-resolution mass spectrometry for molecular analysis. Petroleomics, a new field of study, assigns molecular formulae to the tens of thousands of peaks observed in oil samples, considering ion types, isotope patterns, and properties of heavy oils. Specialized data processing systems and dedicated software, such as Composer, have been developed for molecular analysis of heavy oils, enabling internal calibration without the addition of calibration samples.

Accepted Answer

The new data processing method involves the automatic assignment of molecular formulae and correction of abundances to improve the reliability and quantitative accuracy of data obtained via Fourier transform ion cyclotron resonance mass spectrometry. The technique was utilized to analyze fractions of atmospheric residue, and assigned molecular formulae for each fraction. The algorithm automatically identified nearly 20,000 heavy oil components. Distillation simulation was used to correct the abundances of assigned molecular formulae, and the correction factor indicated that lower boiling point components tended to require higher correction factors. The method was built in MATLAB and is a widely used language for scientific computing.

Accepted Answer

The AR sample was separated into seven fractions for comprehensive analysis. The sample was first separated into the heptane-soluble (maltene) and insoluble (As) fractions via Soxhlet extraction with n-heptane. The maltene fraction was further separated into six fractions (Sa, 1A, 2A, 3A+, Po, and PA) using column chromatography on activated neutral alumina and silica gel. The yield of each fraction is shown in Table 1. The Po and PA fractions are strongly adsorbed on activated neutral alumina, resulting in a loss of recovery yield of 5.4 wt%.

Accepted Answer

For the Sa fraction, ionization was achieved through atmospheric pressure photo-Ag cationization and laser desorption. In this technique, analyte molecules are ionized by the addition of an Ag cation and desorbed by thermal energy transfer from cobalt-powder-absorbed laser light. The analyte molecules were observed as Ag adducts. The laser was set to 70% power, and 500 laser pulses were irradiated at 1000 Hz.

Accepted Answer

The molecular formulae assigned to each heavy oil fraction are corrected by reference to distillation simulations. First, the FT-ICR MS spectra for each fraction are amalgamated by weighting each fraction according to its yield to produce a spectrum representing the whole AR sample. Second, Eq. (1) is used to estimate the boiling point, Tb, of each component from its molecular formula. Then, the distillation properties of the AR sample are estimated based on the boiling point, molecular weight, and molecular abundance of all components. The double-bond equivalence, DBE, represents the degree of unsaturation calculated for CcHhNnOoSsVvNini according to Eq. (2). Third, a boiling point correction factor is estimated by comparing the distillation simulation results and the boiling point estimated from the FT-ICR MS data. The mass distributions of the fractions shown in Fig. 4 do not agree, but the correction factor depends on the boiling point of the target molecule, so correction of the FT-ICR MS data can cause changes in the fraction yields. Therefore, the correction factor estimated from the integrated FT-ICR MS data is applied to each fraction. After the second processing step, the deviation from the simulated distillation is evaluated, and repeating the processing steps improves the correction factor.

Accepted Answer

Isotopic pattern validation is crucial in peak detection as it helps in accurate identification of compounds. In the given section, isotopic peaks were identified for the HC class with a mass error of less than 0.5 ppm, while no isotopic peaks were detected for the N2O2S class. This validation process aids in distinguishing between different classes of compounds and ensures the reliability of the detected peaks. The absence of isotopic peaks for the N2O2S class indicates the need for additional supporting information for accurate identification. Overall, isotopic pattern validation plays a significant role in enhancing the accuracy and reliability of peak detection in mass spectrometry data analysis.

Accepted Answer

The novel data processing method developed in MATLAB enhances the reliability and quantitative accuracy of FT-ICR MS for AR samples. It takes profile data obtained via FT-ICR MS measurement and performs a series of data processing steps to assign molecular formulae. The recalibration process involves validation against isotopic patterns of major elements (C, H, N, O, S) to ensure accurate identification of unknown sample components. The technique also adopts automatic searches to identify heteroatom classes likely to occur, without prior specification of elemental composition. This method was used to characterize AR sample fractions and assign molecular formulae comprehensively. The algorithm interpreted nearly 20,000 heavy oil components, with absolute mean mass errors in the range of 0.15-0.23 ppm, confirming the reliability of the approach. Additionally, the quantitative accuracy was improved by correcting the component detection sensitivity using a distillation simulation, allowing accurate estimation of distillation properties for fractions heavier than 360 degC.

New Data Processing Method for Heavy Oil Components Analyzed by Fourier Transform Ion Cyclotron Resonance Mass Spectrometry

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Most frequently asked questions

1. What is the significance of fouling in the crude distillation unit?

2. What is the new data processing method for heavy oil components analyzed by Fourier transform ion cyclotron resonance mass spectrometry?

3. How was the AR sample separated for comprehensive analysis?

4. What ionization method used for Sa fraction?

5. How is the molecular formula corrected using distillation simulations?

6. What is the significance of isotopic pattern validation in peak detection?

7. How does the novel data processing method improve FT-ICR MS reliability?

References

Heavy Petroleum Composition. 1. Exhaustive Compositional Analysis of Athabasca Bitumen HVGO Distillates by Fourier Transform Ion Cyclotron Resonance Mass Spectrometry: A Definitive Test of the Boduszynski Model

Molecular Characterization of Sulfur Compounds in Venezuela Crude Oil and Its SARA Fractions by Electrospray Ionization Fourier Transform Ion Cyclotron Resonance Mass Spectrometry

Comparison of Atmospheric Pressure Ionization for the Analysis of Heavy Petroleum Fractions with Ion Mobility-Mass Spectrometry

Characterization of Vanadyl and Nickel Porphyrins Enriched from Heavy Residues by Positive-Ion Electrospray Ionization FT-ICR Mass Spectrometry

Functional Isomers in Petroleum Emulsion Interfacial Material Revealed by Ion Mobility Mass Spectrometry and Collision-Induced Dissociation

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