An Investigation of Moving Bed Biofilm Reactor Nitrification during Long‐Term Exposure to Cold Temperatures
Bibliographic record
Abstract
Biological treatment is the most common and economical means of ammonia removal in wastewater; however, nitrification rates can become completely impeded at cold temperatures. Attached growth processes and, specifically, moving bed biofilm reactors (MBBRs) have shown promise with respect to low-temperature nitrification. In this study, two laboratory MBBRs were used to investigate MBBR nitrification rates at 20, 5, and 1 degree C. Furthermore, the solids detached by the MBBR reactors were investigated and Arrhenius temperature correction models used to predict nitrification rates after long-term low-temperature exposure was evaluated. The nitrification rate at 5 degrees C was 66 +/- 3.9% and 64 +/- 3.7% compared to the rate measured at 20 degrees C for reactors 1 and 2, respectively. The nitrification rates at 1 degree C over a 4-month exposure period compared to the rate at 20 degrees C were 18.7 +/- 5.5% and 15.7 +/- 4.7% for the two reactors. The quantity of solids detached from the MBBR biocarriers was low and the mass of biofilm per carrier did not vary significantly at 20 degrees C compared to that after long-term exposure at 1 degree C. Lastly, a temperature correction model based on exposure time to cold temperatures showed a strong correlation to the calculated ammonia removal rates relative to 20 degrees C following a gradual acclimatization period to cold temperatures.
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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 | 0.000 |
| 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 | 0.001 | 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".