Identification of Flow Regime in a Cocurrent Gas-Liquid Upflow Moving Packed Bed Reactor using Gamma Ray Densitometry
Abstract
Flow regime identification in upflow moving packed bed reactors with the conical bottom was investigated using non-invasive gamma-ray densitometry (GRD) measurement technique. Time domain (Standard Deviation), and state space or chaotic methods (Kolmogorov Entropy) are employed on photon count time series of GRD to determine flow regime. The experiments were performed on a pilot plant scale upflow packed bed reactor made of Plexiglas column of 27.94 cm I.D and a total height of 118 cm including plena, and it is packed randomly with 0.3 cm diameter catalyst till 79 cm including the conical bottom. Two axial and various radial position are selected to conduct GRD scanning. The measurements are conducted at superficial liquid (water) velocity 0.017 cm/s and superficial gas (air) velocity in the range of 0.6-7.7 cm/s under these conditions catalyst bed behaves as packed bed. All analysis showed similar flow regime trend, with observed flow regime as bubbly and pulse flow, when compared with flow regime map for upflow packed bed.
Recommended Citation
A. Toukan et al., "Identification of Flow Regime in a Cocurrent Gas-Liquid Upflow Moving Packed Bed Reactor using Gamma Ray Densitometry," Chemical Engineering Science, vol. 168, pp. 380 - 390, Elsevier, Aug 2017.
The definitive version is available at https://doi.org/10.1016/j.ces.2017.04.028
Department(s)
Chemical and Biochemical Engineering
Research Center/Lab(s)
Center for High Performance Computing Research
Keywords and Phrases
Air; Catalysts; Chemical Reactors; Packed Beds; Pilot Plants; Time Domain Analysis; Flow Regime Identification; Gamma Ray Densitometry (GRD); Kolmogorov Entropies; Measurement Techniques; Moving Packed Bed; Standard Deviation; Superficial Liquid; Up-Flow Packed Bed Reactors; Gamma Rays; Two Phase Upflow Moving Packed Bed
International Standard Serial Number (ISSN)
0009-2509
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2017 Elsevier, All rights reserved.
Publication Date
01 Aug 2017