Water reclamation and nitrogen extraction from municipal solid waste landfill leachate
Bibliographic record
Abstract
This study aims to explore several innovative technologies including electrocoagulation, nanofiltration (NF), membrane distillation (MD), and ion exchange resin for clean water extraction and resource recovery from landfill leachate. Our results demonstrate the technical feasibility of water reuse and ammonia recovery from landfill leachate. Electrocoagulation was effective as a pretreatment step for the NF process and could remove most suspended solids and some organic matter. The results show that the combination of NF and MD can produce high-quality water from landfill leachate suitable for reuse applications with respect to heavy metals (with the exception of arsenic) and pharmaceutically active compounds (PhACs). Heavy metal concentrations in the NF permeate were below the values specified by the Australian Guidelines for Water Recycling. PhAC removals by the NF process were in the range of 67–97%. Heavy metals and PhACs were not detectable in the MD distillate. The recovery of ammonia from NF permeate by a strong acid ion exchange resin was also demonstrated.
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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 | 0.001 | 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) | 0.002 | 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".