Research on the treatment of sulfide-containing wastewater from oilwell production using segmented progressive sulfide removal process
Bibliographic record
Abstract
In response to the challenge of sulfide removal from oilwell produced wastewater, a segmented progressive sulfide removal process was adopted to treat the oilwell produced wastewater through UASB bioreactor and biomimetic reactor for segmented progressive reflux treatment. The experimental results showed that under the conditions of adopting the segmented progressive sulfide removal process, adjusting the S/N ratio of 5.2∶2 in the produced wastewater,supplementing a certain amount of HCO3-, and setting the reflux ratio of 2.7∶1, the removal efficiency of sulfide (S2-) in the produced wastewater arrived to 95.8%, and the maximum sulfide volumetric load was 6.46 kg/(m3·d). The removal efficiency of nitrate (NO3--N) reached 99.6%, with the maximum nitrate volumetric load of 1.13 kg/(m3·d). The key metabolite of sulfide metabolism was elemental sulfur, with a recovery rate of 83.8%, which was verified by SEM and XRD simultaneously, and followed by SO42- and S2O32-. While the key metabolite of nitrate metabolism was N2, with a yield of 72.4%, and followed by NO2-. The new process not only solved the problem of efficient removal of sulfide and nitrate from the produced wastewater, but also realized resource recovery(S0) and non-pollution emission of metabolic end products(such as N2).
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".