Bibliographic record
Abstract
An impinging-jet bubble column has been tested for the ozone mass transfer applications in water treatment. Two venturi injectors were utilized to create turbulent gas-liquid jets in the ambient fluid by placing them at an intersecting angle of 125°. The intersecting of the jets caused an increase in the turbulence produced in the ambient fluid and therefore, increased the gasliquid mass transfer rate. The steady-state one-phase axial dispersion model (1P-ADM) was applied to analyze the dissolved ozone concentration profiles measured in the bubble column by fitting these profiles to the predicted profiles, using the 1P-ADM, to determine the column-average overall mass transfer coefficient (k L a) and liquid-phase axial dispersion coefficient (D L ). Two minimization approaches were applied to solve the differential equation representing the 1P-ADM: two-parameter (k L a and D L ) and one-parameter (k L a) minimization techniques. To examine the sensitivity of the predicted k L a to changes in D L , D L was estimated first from the tracer experiments. Then, the one-parameter minimization technique was applied to determine k L a. As a result, k L a varied only slightly (<11%) between the two types of minimization techniques. Using the two-parameter minimization technique, k L a and D L values were correlated to the superficial gas and liquid velocities u G and u L , respectively) by power-law relationships. The following correlations were obtained: k L a = 20.54 u G 1.13 u L 0.07 and D L = 2.94 x 10 2 u G 0.03 u L 0.18 . The 1P-ADM has proven to be an accurate and easy-to use tool for describing the ozonation process in the impinging-jet bubble column as an excellent conformity between the measured and the predicted dissolved ozone concentration profiles was observed. Key words: axial dispersion model, impinging-jet bubble column, mass transfer, ozone.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".