文献类型：期刊文献

中文题名：S Zorb装置原料油的聚类研究

英文题名：CLUSTER ANALYSIS OF S ZORB UNIT FEEDSTOCKS

作者：王杰[1];程顺[1];刘松[2];欧阳福生[1];赵明洋[2]

机构：[1]华东理工大学化工学院石油加工研究所,上海200237;[2]中国石化上海高桥分公司

年份：2020

卷号：51

期号：10

起止页码：94

中文期刊名：石油炼制与化工

外文期刊名：Petroleum Processing and Petrochemicals

收录：CSTPCD;;Scopus;北大核心:【北大核心2017】；CSCD:【CSCD_E2019_2020】；

基金：中国石油化工股份有限公司合同项目(CLY19056)。

语种：中文

中文关键词：SZorb工艺;催化裂化汽油;K-means聚类算法;模糊C均值聚类算法;辛烷值

外文关键词：S Zorb process;FCC gasoline;K-means clustering algorithm;fuzzy C-means clustering algorithm;octane number

摘要：以某S Zorb汽油吸附脱硫装置运行4年的原料油性质数据为基础,通过对工艺和反应机理的分析,筛选出了饱和烃含量、烯烃含量、芳烃含量、硫含量、密度和溴值作为原料油聚类的6个变量。采用MATLAB编程平台,分别使用K-means和模糊C均值聚类算法进行了原料油聚类研究。结果表明这两种算法对S Zorb装置原料油均有较好的分类效果,均将原料油分成了3类。在此基础上,可以针对不同种类的原料油建立产品辛烷值预测模型,从而为寻找降低S Zorb装置汽油辛烷值损失的操作条件提供指导。
S Zorb process is one of the main technologies to remove sulfur in fluid catalytic cracking gasoline.Based on the feedstock property data from a S Zorb gasoline adsorption desulfurization unit for 4 years,the characteristics and reaction mechanism of S Zorb process,the saturated hydrocarbon content,olefin content,aromatic content,sulfur content,density,and bromine number of the feedstocks were selected as the feedstock clustering variables.K-means and fuzzy C-means clustering algorithms were used to cluster the feedstocks by MATLAB programming platform.The results showed that both the K-means and fuzzy C-means clustering algorithms have a good classification effect on the feedstocks of the S Zorb unit and they both classify the raw oil into three categories.On this basis,the octane number prediction models can be established for different kinds of raw oils,which can provide guidance for finding the operating conditions to reduce the octane number loss of gasoline of S Zorb plant.