Operational conditions for successful partial nitrification in a sequencing batch reactor (SBR) based on process kinetics
Bibliographic record
Abstract
The objective of this study is to analyze the factors affecting the performance of partial nitrification in a sequencing batch reactor (SBR) using kinetic models. During the 4-month operation, dissolved oxygen (DO) and influent ammonia concentration were selected as operating variables to evaluate nitrite accumulation. Stable partial nitrification was observed with two conditions, influent ammonia concentration of 190 mg N/L and a DO of 0.6–3.0 mg/L as well as influent ammonia concentration of 100 mg N/L and a DO of 0.15–2.0 mg/L with intermittent aeration. At a DO of 0.6–3.0 mg O2/L and influent ammonia concentration of 90 mg N/L, nitrite-oxidizing bacteria growth was not suppressed. Kinetic parameters were determined or estimated with batch tests and model simulation. The kinetic model predicted the SBR performance well.
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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 | 0.001 |
| 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 | 0.001 | 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".