文献类型：期刊文献

中文题名：Multiple input self-organizing-map ResNet model for optimization of petroleum refinery conversion units

作者：Jiannan Zhu[1];Vladimir Mahalec[2];Chen Fan[1];Minglei Yang[1,3];Feng Qian[1,3]

机构：[1]Key Laboratory of Smart Manufacturing in Energy Chemical Process,Ministry of Education,East China University of Science and Technology,Shanghai 200237,China;[2]Department of Chemical Engineering,McMaster University,Hamilton,Ontario L8S 4L8,Canada;[3]Engineering Research Center of Process System Engineering,Ministry of Education,East China University of Science and Technology,Shanghai 200237,China

年份：2023

卷号：17

期号：6

起止页码：759

中文期刊名：Frontiers of Chemical Science and Engineering

外文期刊名：化学科学与工程前沿（英文版）

收录：CSTPCD;;Scopus;CSCD:【CSCD2023_2024】；PubMed;

基金：supported by the National Natural Science Fund for Distinguished Young Scholars(Grant No.61725301);the National Natural Science Foundation of China(Basic Science Center Program:Grant No.61988101);International(Regional)Cooperation and Exchange Project(Grant No.61720106008);General Program(Grant No.61873093).

语种：英文

中文关键词：hydrocracking;convolutional neural networks;self-organizing map;deep learning;data-driven optimization

摘要：This work introduces a deep-learning network,i.e.,multi-input self-organizing-map ResNet(MISR),for modeling refining units comprised of two reactors and a separation train.The model is comprised of self-organizing-map and the neural network parts.The self-organizing-map part maps the input data into multiple two-dimensional planes and sends them to the neural network part.In the neural network part,residual blocks enhance the convergence and accuracy,ensuring that the structure will not be overfitted easily.Development of the MISR model of hydrocracking unit also benefits from the utilization of prior knowledge of the importance of the input variables for predicting properties of the products.The results show that the proposed MISR structure predicts more accurately the product yields and properties than the previously introduced self-organizing-map convolutional neural network model,thus leading to more accurate optimization of the hydrocracker operation.Moreover,the MISR model has smoother error convergence than the previous model.Optimal operating conditions have been determined via multi-round-particle-swarm and differential evolution algorithms.Numerical experiments show that the MISR model is suitable for modeling nonlinear conversion units which are often encountered in refining and petrochemical plants.