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Comparison of Hydrodynamic Cavitation Devices Based on Linear and Swirling Flows: Degradation of Dichloroaniline in Water

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journal contribution
posted on 14.07.2020, 18:33 by Varaha Prasad Sarvothaman, Alister Simpson, Vivek V. Ranade
Hydrodynamic cavitation (HC) is being increasingly used for a wide range of applications including wastewater 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 °C) 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 a similar pressure drop versus flow rate curve. It was conclusively shown that swirl does not suppress 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 a 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.

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