Degasification of mixed liquor improves settling and biological nutrient removal
Bibliographic record
Abstract
Degasification of mixed liquor by subjecting it to vacuum is a physical process used in biological nutrient removal (BNR) to improve settleability and allow for achieving higher mixed liquor suspended solids (MLSS). Vacuum degassing installation is located between the last cell of the bioreactor and secondary clarifiers. In this process two operations are performed: gas bubbles contained in mixed liquor leaving the bioreactor are removed and concentration of gasses (mainly nitrogen gas) dissolved in the liquid is reduced. Lack of gas bubbles and concentration of dissolved nitrogen gas below saturation in mixed liquor significantly improved sludge settling in secondary clarifiers and eliminated floating scum formation. Presented settleability tests of degasified MLSS and return activated sludge (RAS) from various BNR facilities showed continued settling and/or thickening for over 3 h at room temperature, without exhibiting any solids separation. Settleability tests of biomass that was not degasified typically led to flotation of portion of the sludge after about 1.5 h. Plants equipped with vacuum degasification consistently operate at larger than typically recommended final clarifier sludge surface loading rates. Rates as high as 180-220 kg TSS/m2d and deep sludge blankets have been employed. Such plants were shown to maintain operational levels of MLSS at 4500 to 6000 mg/L and higher.
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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 | 0.000 | 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".