文献类型：期刊文献

英文题名：Data-driven models of crude distillation units for production planning and for operations monitoring

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

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, Peoples R China

年份：2023

卷号：177

外文期刊名：COMPUTERS & CHEMICAL ENGINEERING

收录：;EI(收录号：20232514282263);WOS:【SCI-EXPANDED(收录号:WOS:001034337100001)】；

基金：This work was supported by National Key Research and Development Program of China (2022YFB3305900), National Natural Science Foundation of China (Key Program: 62136003), National Natural Science Foundation of China (62293501), the Programme of Introducing Talents of Discipline to Universities (the 111 Project) under Grant B17017.

语种：英文

外文关键词：Data-driven monitoring models of crude; distillation units; Data-driven planning models of crude; Training data set size vs; data-driven model; structure; PCA-ResNet deep learning model

摘要：This work develops best practices for building consistent data-driven CDU models for production planning and for monitoring. Prior knowledge-based transformations of raw data are shown to be essential to achieve high model accuracy. Data-driven SOM-ResNet and PCA-ResNet deep models, feedforward neural network (FNN), partial least-squares (PLS), and least absolute shrinkage and selection operator (LASSO) are studied. Instead of increasing the dimensionality of data (as in SOM-ResNet), it is better to decrease the dimensionality via PCA and employ deep learning image processing ResNet model. In addition, increasing the number of residual blocks in the PCA-ResNet model increases its accuracy, while adding more hidden layers to FNN does not. With appropriate data selection and transformation, when the number of data samples is less than 500, LASSO model is the best for planning and for monitoring. If the number of data samples exceeds 3,000, then PCA-ResNet model is the best for monitoring.