Short-term and long-term studies of the co-treatment of landfill leachate and municipal wastewater
Bibliographic record
Abstract
The impact of the pre-treatment of landfill leachate on the co-treatment of landfill leachate and municipal wastewater was investigated through a short-term and a long-term study.The short-term study aimed to mimic the shock load of leachate on the wastewater treatment process.The leachate pre-treatment was achieved by coagulation and air stripping to remove partial chemical oxygen demand (COD) and ammonia.The long-term study aimed to investigate the effectiveness of leachate pre-treatment on nutrient removal of the wastewater treatment process in a long-term operational condition when air stripping was used as a means of pretreatment.From the short-term study, it was found that at low mixing ratios (0.5% and 1%), pre-treatment did not produce any significant difference from the one without pre-treatment.When the untreated leachate mixing rate was increased (5% and 10%), the system was not able to achieve full nitrification during one cycle.However, the pre-treatment of leachate lowered the ammonia in the influent, therefore allowing for full nitrification.The long-term study demonstrated that even at a 10% mixing ratio, the high ammonia concentration in the leachate did not have a negative impact on the nitrification process.Due to the high non-readily biodegradable portion of COD in the leachate, the majority of the COD from the leachate ended up in the effluent thereby decreasing the effluent quality.It was found that at a 2.5% mixing ratio of leachate with wastewater, the overall biological nutrient removal process of the system was improved without compromising the COD removal efficiency.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".