Surrogate formulation and molecular characterization of sulfur species in vacuum residues using APPI and ESI FT-ICR mass spectrometry

Abstract

Vacuum residues (VR) are the bottom of the barrel products left after vacuum distillation of crude oils. VR are primarily used as feedstock for production of syn-gas and hydrogen via gasification; and heavy fuel oil (HFO) for use as fuel in power generation and shipping. However, VR contain relatively large amounts of sulfur (upto 8% by mass) and require the removal of varying amounts depending on the emission norms (eg. International Maritime Organization 2020 sulfur regulations). Understanding the fuel molecular structure and, in particular, the structure of sulfur species enables the adoption and optimization of suitable desulfurization strategies. In the present work, detailed molecular characterization of the sulfur species in VR was performed using positive ion atmospheric pressure photoionization (APPI) and electrospray ionization (ESI) coupled to Fourier transform ion cyclotron resonance (FT-ICR) mass spectrometry (MS). Ions possessing mass to charge (m/z) in the range of 100 to 1200 were detected using the ultra-high resolution instrument and were resolved into unique chemical formulas (CcHhSsNnOo). The assigned masses were then divided into molecular classes based on the presence of heteroatoms, and plots of carbon number versus double bond equivalency (DBE) were made for each molecular class. The molecular classes were further sub-divided based on the presence of sulfur families like sulfides (Su), thiophenes (Th), benzothiophenes (BT), dibenzothiophenes (DBT) and benzonaphthothiophene (BNT) and their derivatives. A single surrogate molecule that represents the average structure of the VR sample was then designed based on the average molecular parameters (AMP) obtained from APPI and ESI FT-ICR MS. Plausible core skeletal structures of VR were drawn from the average DBE value, and then a symmetrical, alkylated, polyaromatic sulfur heterocycles (PASH) molecule was formulated as the VR surrogate. A number of physical and thermo-chemical properties of the VR surrogate were then predicted using quantitative structure property relationships (QSPR). The VR surrogate proposed here will enable high-fidelity computational studies, including chemical kinetic modeling, property estimation, and emissions modeling.