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 (kLa) and liquid-phase axial dispersion coefficient (DL). Two minimization approaches were applied to solve the differential equation representing the 1P-ADM: two-parameter (kLa and DL) and one-parameter (kLa) minimization techniques. To examine the sensitivity of the predicted kLa to changes in DL, DL was estimated first from the tracer experiments. Then, the one-parameter minimization technique was applied to determine kLa. As a result, kLa varied only slightly (<11%) between the two types of minimization techniques. Using the two-parameter minimization technique, kLa and DL values were correlated to the superficial gas and liquid velocities uG and uL, respectively) by power-law relationships. The following correlations were obtained: kLa = 20.54 uG1.13uL0.07 and DL = 2.94 x 102uG0.03uL0.18. 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 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".