文献类型：期刊文献

中文题名：Molecular reconstruction of vacuum gas oils using a general molecule library through entropy maximization

作者：Na Wang[1];Chong Peng[2];Zhenmin Cheng[1];Zhiming Zhou[1]

机构：[1]School of Chemical Engineering,East China University of Science and Technology,Shanghai 200237,China;[2]Dalian Research Institute of Petroleum and Petrochemicals,SINOPEC,Dalian 116000,China

年份：2022

卷号：35

期号：8

起止页码：21

中文期刊名：Chinese Journal of Chemical Engineering

外文期刊名：中国化学工程学报（英文版）

收录：CSTPCD;;Scopus;CSCD:【CSCD2021_2022】；PubMed;

基金：supported by the National Natural Science Foundation of China(21978093)。

语种：英文

中文关键词：Vacuum gas oil;Molecular reconstruction;Model;Algorithm;Optimization

摘要：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 naphthenes 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.