Publications by authors named "Varaha Sarvothaman"

Phenols are recalcitrant compounds that constitute the majority of organic contaminants in industrial wastewaters. Their removal at large scales require a combination of various processes to reach the desired discharge quality. An extensive body of work has already been published in the area of phenol removal from wastewater, however none of them have focussed on a truly 'sensible' approach for coupling advanced oxidation processes (AOPs).

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Hydrodynamic Cavitation (HC) offers an attractive platform for intensifying oxidative desulphurization of fuels. In the first part of this work, we present new results on oxidising single ring thiophene in a model fuel over the extended range of volume fraction of organic phase from 2.5 to 80 v/v %.

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Hydrodynamic cavitation (HC) has been extensively investigated for effluent treatment applications. Performance of HC devices or processes is often reported in terms of degradation of organic pollutants rather than quantification of hydroxyl (OH) radicals. In this study, generation of OH radicals in vortex based cavitation device using coumarin dosimetry was quantified.

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Utilising cavitation for enhancing oxidative desulphurization has been investigated for nearly-two decades with recent investigations shifting focus from low-capacity acoustic cavitation (AC) to scalable hydrodynamic cavitation (HC). This work focuses on developing a viable means for removing thiophene's from fuels. In the first phase of this work, use of vortex based HC devices for removal of single and dual ring thiophenes from dodecane was investigated.

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Cavitation events create extreme conditions in a localized 'bubble collapse' region, leading to the formation of hydroxyl radicals, shockwaves and microscopic high-speed jets, which are useful for many chemical and physical transformation processes. Single bubble dynamics equations have been used previously to investigate the chemical and physical effects of cavitation. In the present study, the state of the art of the single bubble dynamics equations was reviewed and certain noteworthy modifications were implemented.

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We have developed artificial neural network (ANN) based models for simulating two application examples of hydrodynamic cavitation (HC) namely, biomass pre-treatment to enhance biogas and degradation of organic pollutants in water. The first case reports data on influence of number of passes through HC reactor on bio-methane generation from bagasse. The second case reports data on influence of HC reactor scale on degradation of dichloroaniline (DCA).

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