Superoxygenation: analysis of oxygen transfer design parameters using high-purity oxygen and a pressurized column
Bibliographic record
Abstract
There remains significant potential for improvement in oxygen transfer efficiency, which can account for 60% of water and wastewater treatment energy requirements. This research examined superoxygenation, or aerating water under pressure with high-purity oxygen gas. Examined were the effects superoxygenation has on five key aeration design parameters: the mass transfer coefficient (KLa), saturation concentration ([Formula: see text]), standard oxygen transfer rate (SOTR), standard aeration efficiency (SAE), and standard oxygen transfer efficiency (SOTE). This research compared values under pressures of 0, 50, 100, 150, and 200 kPa using air and pressure swing adsorption (PSA) generated oxygen. It was found that with increasing pressure for both air and PSA oxygen: KLa decreased, [Formula: see text] increased, SOTR and SAE remained constant, and SOTE increased. While comparing air and PSA oxygen, oxygen was found to have a similar KLa, larger [Formula: see text], SOTR, and SOTE, and a lower SAE. It was concluded that superoxygenation is a viable method for increasing oxygen transfer and could potentially reduce oxygenation costs in water treatment processes.
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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".