文献类型：期刊文献

英文题名：Modeling the Hydrocracking Process with Deep Neural Networks

作者：Song, Wenjiang[2];Mahalec, Vladimir[1];Long, Jian[2];Yang, Minglei[2];Qian, Feng[2]

机构：[1]McMaster Univ, Dept Chem Engn, Hamilton, ON L8S 4L8, Canada;[2]East China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China

年份：2020

卷号：59

期号：7

起止页码：3077

外文期刊名：INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

收录：;EI(收录号：20201008262991);WOS:【SCI-EXPANDED(收录号:WOS:000515213800043)】；

基金：This work is supported by the National Natural Science Foundation of China (Basic Science Center Program, no. 61988101), International (Regional) Cooperation and Exchange Project (no. 61720106008), National Natural Science Foundation of China (Major Program: nos. 61590923, 61973124, and 61873093), Programme of Introducing Talents of Discipline to Universities (the 111 Project) under grant no. B17017, and Fundamental Research Funds for the Central Universities, no. 222201917006.

语种：英文

外文关键词：Refining - Hydrocracking - Self organizing maps - Conformal mapping - Convolutional neural networks - Deep neural networks

摘要：In the refinery process, a vast amount of data is generated in daily production. How to make full use of these data to improve the simulation's accuracy is crucial to enhancing the refinery operating level. In this paper, a novel deep learning framework integrating the self-organizing map (SOM) and the convolutional neural network (CNN) is developed for modeling the industrial hydrocracking process. The SOM is used to map input variables into two-dimensional maps to extract process features. Then, these maps are fed into the CNN to predict the outputs of the hydrocracking process. The SOM adopted is free of training, which reduces the computational complexity, simplifies the application, and improves the prediction accuracy. Practical guidance on the application of the proposed framework is provided by comparing and analyzing different structures and parameters. Finally, an online modeling scheme is developed and applied in an actual hydrocracking process. Experimental results demonstrate that the proposed framework has great performance in modeling the hydrocracking process and provides a good reference for process optimization.