Molecular reconstruction of vacuum gas oils using a general molecule library through entropy maximization

Vacuum gas oil (VGO) is the most important feedstock for hydrocracking processes in refineries, but its molecular composition cannot be fully acquired by current analysis techniques owing to its complexity. In order to build an accurate and reliable molecular-level kinetic model for reactor design and process optimization, the molecular composition of VGO has to be reconstructed based on limited measurements. In this study, a modified stochastic reconstruction-entropy maximization (SR-REM) algorithm was applied to reconstruct VGOs, with generation of a general molecule library once and for all via the SR method at the first step and adjustment of the molecular abundance of various VGOs via the REM method at the second step. The universality of the molecule library and the effectiveness of the modified SR-REM method were validated by fifteen VGOs (three from the literature) from different geographic regions of the world and with different properties. The simulated properties (density, elemental composition, paraffin-naphthene-aromatics distribution, boiling point distribution, detailed composition of naph-thenes and aromatics in terms of ring number as well as composition of S-heterocycles) are in good agreement with the measured counterparts, showing average absolute relative errors of below 10% for each property. (c) 2021 Chemical Industry and Engineering Society of China, and Chemical Industry Press Co., Ltd. All rights reserved.

Automated summary

VGO, the key feedstock for hydrocracking processes in refineries, has a complex molecular composition that can be accurately reconstructed using the modified SR-REM algorithm. Validation of the method shows accurate simulated properties, providing important insights for process optimization in refineries.