文献类型：期刊文献

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

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

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]McMaster Univ, Dept Chem Engn, Hamilton, ON L8S 4L8, Canada;[3]East China Univ Sci & Technol, Engn Res Ctr Proc Syst Engn, Minist Educ, Shanghai 200237, Peoples R China

年份：2023

卷号：17

期号：6

起止页码：759

外文期刊名：FRONTIERS OF CHEMICAL SCIENCE AND ENGINEERING

收录：;EI(收录号：20231013672320);WOS:【SCI-EXPANDED(收录号:WOS:000946813800005)】；

基金：This work was 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), and General Program (Grant No. 61873093).

语种：英文

外文关键词：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.