Nitrification of Petroleum Extraction Produced Water: Salt Concentrations and Nitrifying Activity
Bibliographic record
Abstract
Abstract Oil and gas exploration activities generate a considerable wastewater, among which produced water is the most relevant. The chemical complexity of this stream (high ammonium and salt concentrations) adversely affects biological treatment and can be detrimental to sensitive organisms such as nitrifiers. This study addressed the nitrification of produced water from a Brazilian oil extraction platform. Laboratory-scale experiments have shown that nitrification activity could not be sustained with raw produced water in the long term. Nevertheless, when produced water was appropriately diluted, detrimental effects on nitrification were significantly reduced. Experiments evaluating effects of increasing salt concentrations on nitrification have shown that complete ammonium removal were achieved even at very high salt concentrations (up to 100 g NaCl/L). At 125 g NaCl/L, however, nitrifiers were completely inhibited and negligible ammonium removal was observed. Additional tests with no biomass and under similar operational conditions to those applied in previous experiments confirmed that biological nitrification was the most important mechanism of ammonium removal.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.001 | 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".