基于图卷积神经网络汽油单体烃辛烷值的预测  

GRAPH CONVOLUTION NEURAL NETWORK FOR PREDICTING THE OCTANE NUMBER OF PURE HYDROCARBONS IN GASOLINE

作　　者：崔晨 何杉 吕文进 张霖宙 周祥[1] Cui Chen;He Shan;LüWenjin;Zhang Linzhou;Zhou Xiang(SINOPEC Research Institute of Petroleum Processing,Beijing 100083;State Key Laboratory of Heavy Oil Processing,China University of Petroleum)

机构地区：[1]中国石化石油化工科学研究院,北京100083 [2]中国石油大学(北京)重质油国家重点实验室

出　　处：《石油炼制与化工》2021年第7期82-87,共6页Petroleum Processing and Petrochemicals

基　　金：国家重点研发计划项目(2017YFB0306501)。

摘　　要：基于图卷积神经网络的神经指纹方法,引入了池化操作,建立了改进的神经指纹方法;进而采用改进的神经指纹法建立了汽油单体烃辛烷值的预测模型,作为对分子级汽油辛烷值调合模型的支撑。通过用单体烃沸点和临界温度数据集对预测模型进行验证,发现池化操作的引入对神经指纹法的预测能力有明显提升,改进神经指纹法模型可自动选取对辛烷值有利和不利的结构特征,双键结构对单体烃马达法辛烷值的影响比芳环结构的影响更大。该预测模型对研究法辛烷值和马达法辛烷值的预测达到了同等水平,取得了良好的预测效果。Based on the graph convolution neural network,an improved neural fingerprint method was established by introducing pooling operation.A model for predicting octane number of pure hydrocarbons in gasoline was established by this method as an important part of blending model of gasoline on molecular level.Through the verification on the boiling point and critical temperature data set,the prediction ability of neural fingerprint method was improved obviously by introducing pooling operation.The favorable and unfavorable features could be selected automatically by the improved neural fingerprint method.It was also found that the effect of double bond on motor octane number was greater than that of aromatic ring.The improved neural fingerprint method also had good results in the prediction of both research octane number and motor octane number of pure hydrocarbons in gasoline.

关 键 词：图卷积神经网络 神经指纹 辛烷值 汽油 

分 类 号：TE626.21[石油与天然气工程—油气加工工程] TP183[自动化与计算机技术—控制理论与控制工程]