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


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.


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

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Document Type

Article - Journal

Document Version


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© 2020 Elsevier, All rights reserved.

Publication Date

01 Dec 2020