Sludge remnant treatment based on ultrasound
Bibliographic record
Abstract
ABSTRACT To solve remaining sludge handling problems, ultrasonic waves were used. In the low C/N wastewater treatment process, a large carbon source needed to be added to maintain a certain nitrogen and phosphorus removal efficiency. After the remaining sludge was dissolved and broken, the internal carbon source was used as an external carbon source, flowing back into the main denitrification process. The change in nitrogen and phosphorus removal efficiency, the reduction of residual sludge and the effect of resource utilization were studied. The residual sludge was pretreated by ultrasonic waves. Under the ultrasonic action, the cell wall of the remaining sludge was cracked, and the contents were released into the system. The SCOD, ammonia nitrogen and total nitrogen in the system greatly increased. An A/O device was used for the rapid acclimation of the denitrification sludge, and the final influent index was 180 mg/L, and the COD was 1200 mg/L. Total phosphorus was 17 mg/L, and the ammonia nitrogen in the effluent was less than 1 mg/L. COD was less than 50 mg/L. The total phosphorus was less than 1 mg/L. The total nitrogen removal rate was about 86%. Regarding sludge reduction, the experimental group accumulated a 783.2 g discharge of residual sludge, while the sludge yield was 0.131 g-MLSS/g-COD, achieving a sludge reduction rate of 23.20%. Therefore, the system can effectively reduce excess sludge.
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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".