文献类型：期刊文献

中文题名：基于BP神经网络和遗传算法优化S Zorb装置汽油辛烷值损失

英文题名：DECREASING OCTANE NUMBER LOSS OF PRODUCT GASOLINE FROM S Zorb UNIT USING BP NEURAL NETWORK WITH GENETIC ALGORITHM

作者：高萍[1];刘松[2];程顺[1];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：2021

卷号：52

期号：7

起止页码：88

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2020】；CSCD:【CSCD_E2021_2022】；

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：S;Zorb工艺;辛烷值损失;BP神经网络;模糊C均值聚类算法;遗传算法

外文关键词：S Zorb process;octane number loss;BP neural network;fuzzy C-means clustering algorithm;genetic algorithm

摘要：催化裂化汽油在精制改质过程中通常存在一定幅度的辛烷值损失。以国内某石化企业1.2 Mt a S Zorb装置多年数据为基础,使用灰色关联分析和SPSS方法从包括原料油性质、吸附剂性质、产品性质和操作变量在内的368个变量中筛选出22个建模变量。在通过模糊C均值聚类算法将原料油分为3类的基础上,分别建立了结构为21-20-1,21-18-1,21-17-1的预测产品研究法辛烷值(RON)的BP神经网络模型。结果表明,所建立的3种模型预测效果良好。将所建立的RON预测模型与遗传算法相结合,在保证汽油脱硫效果的前提下,可以明显降低产品汽油RON损失,对实际工业生产具有参考作用。
There is usually a certain degree of octane number loss in FCC gasoline upgrading process.Based on the data over the years from a domestic S Zorb unit,21 modeling variables from a total of 368 variables including the properties of feedstock,adsorbent,product and operating variables were screened out by grey correlation analysis and SPSS method.On the basis of clustering the feedstocks into three categories by fuzzy C-means clustering algorithm,the 21-20-1,21-18-1,and 21-17-1 types of BP neural network models were established respectively.The verification results indicated that the accuracies of the three models established were high.The BP neural network models and genetic algorithm were combined to optimize the operating conditions for reducing the octane number loss of product on the premise of ensuring the desulfurization effect of gasoline.The predicted results can give an important reference for industrial production.