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Estimation of Gas Holdup Using the Gassed to Ungassed Power Ratio of an Oxygen–Water System in a Stirred and Sparged Tank Contactor
Author(s) -
Sudip K. Ganguly,
Akash Thakare,
Deepak P. Patil,
Aditi Tibrewal,
Anuj Dethe,
C. B. Majumder,
Anjan Ray
Publication year - 2020
Publication title -
acs omega
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.779
H-Index - 40
ISSN - 2470-1343
DOI - 10.1021/acsomega.0c02292
Subject(s) - sparging , impeller , contactor , mechanics , power (physics) , continuous stirred tank reactor , volumetric flow rate , chemistry , analytical chemistry (journal) , materials science , thermodynamics , chromatography , control theory (sociology) , mechanical engineering , physics , engineering , computer science , control (management) , artificial intelligence
Gas holdup (ε g ) and power correlations in gas-liquid (G-L) systems, apart from the physicochemical properties of the liquid phase, are dependent on impeller-sparger-vessel geometry. To date, reported correlations do not specifically address this issue, and it must be investigated with a unified approach. Here, we propose a correlation via the use of a normalized ε g that involves the impeller-sparger system geometry for a vessel of standard geometry expressed as a function of an easily measurable and independent operational parameter, that is, (1 - P g / P l ), where P g / P l is the gassed to ungassed power ratio. Furthermore, our work demonstrates that P g / P l can be used as a tool for the identification of hydrodynamic regimes. Radial and axial impellers with ring spargers were used in a stirred and sparged contactor (SSTC) of 0.25 m diameter containing 1 × 10 -2 m 3 water. The oxygen flowrate ( Q g ) was varied from 2.5 to 40 LPM or (4.17 to 66.7) × 10 -5 m 3 s -1 , and the agitation intensity ( N 0 ) was varied from 1.67 to 50 rps at the temperature (θ) = 313 K under atmospheric pressure. This novel correlation is easy to use, offers reasonable precision, and can serve as a valuable alternative to more complex correlation models.

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