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Comparison of hydrodynamic cavitation devices based on linear and swirling flows: degradation of dichloroaniline in water
journal contribution
posted on 2021-01-13, 15:33 authored by Varaha Prasad Sarvothaman, Alister Simpson, Vivek V. RanadeHydrodynamic cavitation (HC) is being increasingly used for a wide range of applications including
waste water treatment. No systematic comparison of pollutant degradation performance of different
HC devices is available. In this work, for the first time: a basis for comparing performance of HC devices
and a systematic comparison of pollutant degradation performance of five different types of HC
devices based on linear and swirling flows is presented. 2,4 dichloroaniline (DCA) was selected as a
model pollutant in water as it contains multiple functional groups on an aromatic ring. Experiments
were performed at two values of pressure drop across HC devices (100 and 200 kPa) at a constant
initial concentration (35 ppm), pH (7) and temperature (18 oC) for five types of HC devices namely
orifice, venturi, orifice with swirl, venturi with swirl and vortex diode. The pollutant degradation was
interpreted by a per-pass degradation factor approach. The study demonstrated that five different
types of cavitation devices performed similar to each other when these devices were designed to
exhibit similar pressure drop versus flow rate curve. It was conclusively shown that swirl does not
supress degradation performance while offering advantages on shielding device walls from collapsing
cavities. This is an important and new result which will be useful for selecting and designing cavitation
devices. Pollutant degradation data for geometrically similar vortex diodes of two smaller scales
showed significantly higher degradation performance. The number of passes required for ~10%
degradation for the devices with nominal capacity of 1, 5 and 20 LPM were 15, 100 and 1200 passes
respectively. The presented experimental data from these seven devices will be useful for evaluating
computational models and hopefully stimulate further development of predictive computational
models in this challenging area.
History
Publication
Industrial & Engineering Chemistry Research;59 (30), pp. 13841-13847Publisher
American Chemical SocietyNote
peer-reviewedRights
© 2020 ACS This document is the Accepted Manuscript version of a Published Work that appeared in final form in Industrial & Engineering Chemistry Research, copyright © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see https://doi.org/10.1021/acs.iecr.0c02125Language
EnglishExternal identifier
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