Molecular reconstruction of vacuum residue based on high-resolution mass spectrometric characterization

Abstract

Accurately obtaining the molecular composition of vacuum residue (VR) is of great significance for feedstock screening, process development, and operational optimization. However, due to the complex molecular composition of VR, traditional molecular reconstruction models based on bulk properties struggle to accurately capture the characteristics of the molecular distribution. To address this, this study proposed a precise molecular reconstruction method for VR based on high-resolution mass spectrometry (HRMS) data. Using molecular-level characterization data obtained from HRMS as a reference, a method for the compositional similarity between the calculated and experimental molecular distribution was introduced. The compositional similarity was incorporated into the global optimization algorithm that simultaneously matched both bulk properties and molecular composition, effectively constraining the distribution of both hydrocarbon and heteroatom-containing species. The method was applied to five inferior VRs from different sources. The results demonstrate that the developed model can accurately calculate the VR bulk properties. Moreover, the predicted molecular distribution agreed well with experimental measurements, and the content of over 20000 molecules in VR was quantitatively predicted. This approach reduced the multi-solution problem inherent in the traditional molecular reconstruction method, providing a precise foundational model for molecular-level process optimization and regulation.