Containment of Biogenic Sulfide Production in Continuous Up‐Flow Packed‐Bed Bioreactors with Nitrate or Nitrite
Bibliographic record
Abstract
Produced water from the Coleville oil field in Saskatchewan, Canada was used to inoculate continuous up-flow packed-bed bioreactors. When 7.8 mM sulfate and 25 mM lactate were present in the in-flowing medium, H(2)S production (souring) by sulfate-reducing bacteria (SRB) was prevented by addition of 17.5 mM nitrate or 20 mM nitrite. Changing the sulfate or lactate concentration of the in-flowing medium indicated that the concentrations of nitrate or nitrite required for containment of souring decreased proportionally with a lowered concentration of the electron donor lactate, while the sulfate concentration of the medium had no effect. Microbial communities were dominated by SRB. Nitrate addition did not give rise to changes in community composition, indicating that lactate oxidation and H(2)S removal were caused by the combined action of SRB and nitrate-reducing, sulfide-oxidizing bacteria (NR-SOB). Apparently the nitrite concentrations formed by these NR-SOB did not inhibit the SRB sufficiently to cause community shifts. In contrast, significant community shifts were observed upon direct addition of high concentrations (20 mM) of nitrite. Strains NO3A and NO2B, two newly isolated, nitrate-reducing bacteria (NRB) emerged as major community members. These were found to belong to the epsilon-division of the Proteobacteria, to be most closely related to Campylobacter lari, and to oxidize lactate with nitrate or nitrite as the electron acceptor. Thus the mechanism of microbial H(2)S removal in up-flow packed-bed bioreactors depended on whether nitrate (SRB/NR-SOB) or nitrite (SRB/NR-SOB as well as NRB) was used. However, the amount of nitrate or nitrite needed to completely remove H(2)S was dictated by the electron donor (lactate) concentration, irrespective of mechanism.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".