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Maldistribution and Dynamic Liquid Holdup Quantification of Quadrilobe Catalyst in a Trickle Bed Reactor using Gamma-Ray Computed Tomography: Pseudo-3D Modelling and Empirical Modelling using Deep Neural Network
Chemical Engineering Research and Design
  • Binbin Qi
  • Omar Farid
  • Sebastián Uribe
  • Muthanna H. Al-Dahhan, Missouri University of Science and Technology
Abstract

The dynamic liquid distribution and holdup in a TBR packed with porous quadrilobe catalyst were studied using advanced Gamma-ray Computed Tomography. A multi-compartment module is used to quantify the maldistribution factor which shows that there is a transition region from high maldistribution to relatively uniform distribution depending on the flowrates. The 3D maldistribution maps show that there is more dynamic liquid close to the column center at high bed height and there is no high correlation between the average dynamic liquid holdup and the bed height. If the gas flowrate increases while keeping the liquid flowrate fixed, the average dynamic liquid holdup decreases; however, if the gas flowrate is fixed, there is no dominant increasing or decreasing trend showing up. A Deep Neural Network model and a pseudo-3D model are developed showing high accuracy for predicting the local dynamic liquid holdup at different bed heights, radius, and flowrates.

Department(s)
Chemical and Biochemical Engineering
Research Center/Lab(s)
Center for High Performance Computing Research
Keywords and Phrases
  • Deep Neural Network,
  • Gamma-Ray CT,
  • Liquid Holdup Modeling,
  • Maldistribution,
  • Quadrilobe Catalyst,
  • Trickle Bed Reactor
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2020 Elsevier, All rights reserved.
Publication Date
12-1-2020
Publication Date
01 Dec 2020
Disciplines
Citation Information
Binbin Qi, Omar Farid, Sebastián Uribe and Muthanna H. Al-Dahhan. "Maldistribution and Dynamic Liquid Holdup Quantification of Quadrilobe Catalyst in a Trickle Bed Reactor using Gamma-Ray Computed Tomography: Pseudo-3D Modelling and Empirical Modelling using Deep Neural Network" Chemical Engineering Research and Design Vol. 164 (2020) p. 195 - 208 ISSN: 0263-8762
Available at: http://works.bepress.com/muthanna-al-dahhan/117/