文献类型：期刊文献

英文题名：A hybrid deep learning and mechanistic kinetics model for the prediction of fluid catalytic cracking performance

作者：Yang, Fan[1];Dai, Chaonan[1];Tang, Jianquan[1];Xuan, Jin[2];Cao, Jun[3]

机构：[1]Lenovo Grp, Data Intelligence Applicat Lab, Chengdu 610041, Peoples R China;[2]Loughborough Univ, Dept Chem Engn, Loughborough, Leics, England;[3]East China Univ Sci & Technol, Sch Mech & Power Engn, Shanghai 200237, Peoples R China

年份：2020

卷号：155

起止页码：202

外文期刊名：CHEMICAL ENGINEERING RESEARCH & DESIGN

收录：;EI(收录号：20200408076652);WOS:【SCI-EXPANDED(收录号:WOS:000516887800019)】；

基金：This research work is supported by the Shanghai Natural Science Foundation (18ZR1409000), the Fundamental Research Funds for the Central Universities of China (No. 222201714048) and the UK Engineering and Physical Sciences Research Council (EPSRC) via grant number EP/R012164/2.

语种：英文

外文关键词：Fluidic catalytic cracking; Deep neural network; Lumped kinetics model; Hybrid model; Artificial intelligence; Machine learning

摘要：Fluid catalytic cracking (FCC) is one of the most important processes in the renewable energy as well as petrochemical industries. The prediction and understanding-of the FCC performance in a real industrial environment is still challenging, as this is a highly complex process affected by many extremely non-linear and interrelated factors. In this paper, a novel hybrid predictive framework for FCC is developed by integrating a data-driven deep neural network with a physically meaningful lumped kinetic model, powered by orders of magnitude greater number of high-quality data from a modem automated FCC process. The results show that the novel hybrid model exhibits best predictions with regards to all the evaluation criteria such as Mean Absolute Percentage Error, Pearson coefficient, and standard deviation. It indicates that the hybrid data-driven deep learning with mechanistic kinetics model creates a better approach for fast prediction and optimization of complex reaction processes such as FCC. (C) 2020 Institution of Chemical Engineers. Published by Elsevier By. All rights reserved.