Incorporating membrane gas diffusion into a membrane bioreactor for hydrogenotrophic denitrification of groundwater
Bibliographic record
Abstract
A hydrogenotrophic denitrification system, comprising a suspended growth membrane bioreactor (MBR) with membrane hydrogen gas diffusion, was developed to remove nitrate from groundwater. A hollow fiber gas permeable membrane module was designed for hydrogen delivery and a commercially available hollow fiber membrane module was used for solid/liquid separation. The MBR was operated at an SRT of 20 days and at room temperature. Four nitrate loading rates of 24, 48, 96 and 192 NO3(-)-N mg I(-1) d(-1) were applied to the system. As the nitrate loading was raised, pH increased due to increased denitrification and release of OH- ions. The oxidation reduction potential (ORP) remained fairly stable when full denitrification was achieved, but increased when nitrate loading rates reached 192 NO3(-)-N mg I(-1) d(-1) and residual nitrate was present in the reactor. Nitrate removal was complete (100%) in the first three nitrate loadings and 72% in the system with 192 NO3(-)-N mg I(-1) d(-1). Nitrate utilization rates of 30.6, 23.4, and 37.7 g NO3(-)-N m(-3) d(-1) were achieved in the first three loadings. Average effluent dissolved organic carbon (DOC) concentration of approximately 8 mg l(-1) was observed in all four nitrate loading regimes, possibly owing to the generation and release of soluble microbial bi-products (SMP).
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".