Gas Phase Dispersion/Mixing Investigation in a Representative Geometry of Gas-Liquid Upflow Moving Bed Hydrotreater Reactor (MBR) using Developed Gas Tracer Technique and Method based on Convolution/regression
Gas dispersion studies has been executed for the catalyst bed section of a representative geometry of scaled-down industrial Moving Bed Hydrotreater Reactor (MBR). The catalyst bed of MBR is modeled using Axial Dispersion Model (ADM) and its parameters gas dispersion coefficient (Dg) and Peclet number (Pe) are estimated using Residence Time Distribution (RTD) and implementing a methodology based on convolution and regression. Additionally, dimensionless variance (σ2D) for the catalyst bed is also measured using RTDs first and second moments to compare with those findings of ADM model. This study is conducted at the varying flow rates of gas and liquid including scaled down operating conditions. The results of Dg, Pe, and σ2D indicate that bed behaves as a packed bed for low liquid flow rate and moves towards three-phase fluidized bed for increasing liquid flow rate. Overall the gas phase behavior is seen to be in plug flow for all the operating conditions, with relatively high dispersion/mixing in packed bed state. Scaled down flow conditions is seen to be best in terms of gas dispersion/mixing and catalyst utilization.
V. Alexander et al., "Gas Phase Dispersion/Mixing Investigation in a Representative Geometry of Gas-Liquid Upflow Moving Bed Hydrotreater Reactor (MBR) using Developed Gas Tracer Technique and Method based on Convolution/regression," Chemical Engineering Science, vol. 195, pp. 671-682, Elsevier Ltd, Feb 2019.
The definitive version is available at https://doi.org/10.1016/j.ces.2018.10.013
Chemical and Biochemical Engineering
Center for High Performance Computing Research
Keywords and Phrases
Axial Dispersion Model (ADM); Gas dispersion coefficient; Gas tracer; Moving Bed Reactor (MBR); Peclet number; Residence Time Distribution (RTD)
International Standard Serial Number (ISSN)
Article - Journal
© 2019 Elsevier Ltd, All rights reserved.
01 Feb 2019