Scale-up of vortex based hydrodynamic cavitation devices: A case of degradation of di-chloro aniline in water
Bibliographic record
Abstract
Hydrodynamic cavitation (HC) is being increasingly used in a wide range of applications. Unlike ultrasonic cavitation, HC is scalable and has been used at large scale industrial applications. However, no information about influence of scale on performance of HC is available in the open literature. In this work, we present for the first time, experimental data on use of HC for degradation of complex organic pollutants in water on four different scales (~200 times scale-up in terms of capacity). Vortex based HC devices offer various advantages like early inception, high cavitational yield and significantly lower propensity to clogging and erosion. We have used vortex based HC devices in this work. 2,4 dichloroaniline (DCA) – an aromatic compound with multiple functional groups was considered as a model pollutant. Degradation of DCA in water was performed using vortex-based HC devices with characteristic throat dimension, dt as 3, 6, 12 and 38 mm with scale-up of almost 200 time based on the flow rates (1.3 to 247 LPM). Considering the experimental constraints on operating the largest scale HC device, the experimental data is presented here at only one value of pressure drop across HC device (280 kPa). A previously used per-pass degradation model was extended to describe the experimental data for the pollutant used in this study and a generalised form is presented. The degradation performance was found to decrease with increase in the scale and then plateaus. Appropriate correlation was developed based on the experimental data. The developed approach and presented results provide a sound basis and a data set for further development of comprehensive multi-scale modelling of HC devices.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".