Retrofitting conventional primary clarifiers to activated primary clarifiers to enhance nutrient removal and energy conservation in WWTPs in Beijing, China
Bibliographic record
Abstract
Biological nutrient removal requires sufficient carbon source. Meanwhile, the removal of organic matter in wastewater requires energy consumption in the aeration tank. Carbon source for nutrient removal in most wastewater treatment plants with conventional primary clarifier (CPC) is generally insufficient in China. In order to increase carbon source and to save energy, a part of the CPC may be retrofitted as an activated primary clarifier (APC). In this paper, a pilot scale experiment was conducted to examine the performance of primary sludge fermentation and its effect on nitrogen and phosphorus removal. Results show that the primary sludge fermentation in APC has produced a similar VFA/TP ratio but a higher BOD5/TN ratio compared with those in the CPC effluent, and the TN concentrations in the secondary effluent are at 8.0, 10.8, and 17.4 mg/L, while TP is at 0.45, 1.10, and 2.28 mg/L when the pilot test system was fed with (1) the APC effluent, (2) 50% from the APC effluent and 50% from the CPC effluent, and (3) the CPC effluent, respectively. Results also indicate that the BOD5/TN ratio is a more sensitive factor than the VFA/TP ratio for nutrient removal and energy conservation for the APC fermentation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".