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.
B. Qi et al., "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, pp. 195-208, Elsevier, Dec 2020.
The definitive version is available at https://doi.org/10.1016/j.cherd.2020.09.024
Chemical and Biochemical Engineering
Keywords and Phrases
Deep Neural Network; Gamma-Ray CT; Liquid Holdup Modeling; Maldistribution; Quadrilobe Catalyst; Trickle Bed Reactor
International Standard Serial Number (ISSN)
Article - Journal
© 2020 Elsevier, All rights reserved.
01 Dec 2020