Journal
CHEMICAL ENGINEERING RESEARCH & DESIGN
Volume 155, Issue -, Pages 202-210Publisher
ELSEVIER
DOI: 10.1016/j.cherd.2020.01.013
Keywords
Fluidic catalytic cracking; Deep neural network; Lumped kinetics model; Hybrid model; Artificial intelligence; Machine learning
Categories
Funding
- Shanghai Natural Science Foundation [18ZR1409000]
- Fundamental Research Funds for the Central Universities of China [222201714048]
- UK Engineering and Physical Sciences Research Council (EPSRC) [EP/R012164/2]
- EPSRC [EP/R012164/1, EP/R012164/2] Funding Source: UKRI
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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.
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