Integration of Phase Distribution from Gamma-Ray Tomography Technique with Monolith Reactor Scale Modeling
Abstract
In this work, a monolith reactor model was developed to study the effect of phase distribution on the performance of the monolith reactors by integrating phase distribution data from the experiments. To obtain the phase distribution at the reactions conditions, gamma ray computed tomography (CT) was used. Effect of gas density, surface tension of liquid and operating conditions on the phase distribution were studied using gamma-ray computed tomography. Experiments were conducted in Taylor flow regime. With the increase in gas density, uniformity increases. Surface tension has little effect on the distribution in the investigated conditions. Liquid with lower surface tension and gas with lower density has higher cross-sectional liquid saturation. Further, the monolith reactor model with uniform phase distribution and actual distribution were compared. It was found that at higher velocities both gives the same reactor performance irrespective of the maldistribution, however at low velocities, they differ significantly due to the maldistribution. If the maldistribution is present at both low and high velocities, catalyst utilization plays a major role, thus it is recommended to operate at higher velocities where catalyst utilization is high due to high mass transfer.
Recommended Citation
S. Roy et al., "Integration of Phase Distribution from Gamma-Ray Tomography Technique with Monolith Reactor Scale Modeling," Chemical Engineering Science, vol. 200, pp. 27 - 37, Elsevier Ltd, Jun 2019.
The definitive version is available at https://doi.org/10.1016/j.ces.2018.12.053
Department(s)
Chemical and Biochemical Engineering
Research Center/Lab(s)
Center for High Performance Computing Research
Keywords and Phrases
CT; Monoliths; Phase distribution; Reactor modeling
International Standard Serial Number (ISSN)
0009-2509
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2019 Elsevier Ltd, All rights reserved.
Publication Date
01 Jun 2019
Comments
The authors would like to acknowledge financial support from Bayer AG and other CREL sponsors made this work possible.