Mass Transfer of H2S and O2 in Falling Sewage: Contributions via Droplets, Jet, and Bottom Pool
Bibliographic record
Abstract
In urban drainage systems, falling sewage in drop structures expedites the mass transfer of hydrogen sulfide (H2S) and oxygen (O2). This process is important for sewer odor and corrosion control; however, direct experiments and relevant knowledge are limited. This study conducted laboratory experiments using two typical forms of falling sewage: free-falling jet and attached-falling jet. The results show that mass transfer coefficient KL and concentration deficit ratio r (the ratio of upstream-to-downstream gas concentration deficiency) increase with an increase of sewage drop height (0.2–1.4 m) and with a decrease of flow rate (0.9–2.0 L/min). Nonlinear correlations between r and the hydraulic parameters were proposed. The free-falling jet contributed about 40% more mass transfer than the attached-falling jet. The mass transfer rate in free-falling drop structures of this study was 3–13 times that in gravity sewers without drop structures. In addition, O2 is an appropriate surrogate gas for studying H2S mass transfer. Finally, the mass transfer of O2 in a prototype drop structure was estimated: if the drop height is <3 m, the jet may not break up, the mass transfer at the jet surface can be neglected, and almost all the mass transfer happens at the bottom pool of the drop structure; however, if the drop height is >6 m, falling droplets are the main (>80%) contributor. This study provides a tool for estimating the mass transfer in drop structures, which can optimize the design of drop structures to control sewer odor and pipe corrosion.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".