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

journal contribution
posted on 04.05.2020 by Fan Yang, Chaonan Dai, Jianquan Tang, Jin Xuan, Jun Cao
© 2020 Institution of Chemical Engineers 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.

Funding

Shanghai Natural Science Foundation (18ZR1409000)

Fundamental Research Funds for the Central Universities of China (No. 222201714048)

UK Engineering and Physical Sciences Research Council (EPSRC) via grant number EP/R012164/2

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Chemical Engineering

Published in

Chemical Engineering Research and Design

Volume

155

Pages

202 - 210

Publisher

Elsevier BV

Version

AM (Accepted Manuscript)

Rights holder

© Elsevier

Publisher statement

This paper was accepted for publication in the journal Chemical Engineering Research and Design and the definitive published version is available at https://doi.org/10.1016/j.cherd.2020.01.013

Acceptance date

12/01/2020

Publication date

2020-01-21

Copyright date

2020

ISSN

0263-8762

eISSN

1744-3563

Language

en

Depositor

Prof Jin Xuan. Deposit date: 4 May 2020

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