Passive treatment of municipal landfill leachate in a granular drainage layer
Bibliographic record
Abstract
This paper describes a laboratory investigation that simulates in situ treatment of leachate representative of that generated by a municipal solid waste (MSW) landfill. The objective of the investigation is to demonstrate that recycle of leachate and treatment within a leachate collection system (LCS) coupled with a nitrification reactor can provide significant decreases in leachate chemical oxygen demand (COD) and ammonia concentration. In the investigation five 15 cm (6 in.) diameter polyvinyl chloride (PVC) columns were packed with drainage media of various sizes consisting of natural gravel and (or) crushed concrete (washed and screened). Geotextiles were placed between the various media as filterseparators and to promote bacterial growth. Synthetic leachate was continuously fed into the top of each column and recirculated from the bottom at rates representative of operating field conditions. For each column, effluent was discharged to a nitrification reactor before recirculation. The tests were conducted under anaerobic and unsaturated conditions in the columns. The results obtained from the experiment show that in situ treatment at landfills using this approach is viable. A 97% decrease in COD and about 98% conversion of the ammonia to nitrogen gas was observed. The denitrification process did not significantly inhibit COD depletion or methane production. Further, the biogas produced contained only a small fraction of CO2, which could suggest a potential benefit in terms of decreasing the potential of clogging in the LCS and extending the service life of a landfill gas (LFG) collection network by generating a less corrosive environment.Key words: leachate treatment, chemical oxygen demand (COD) removal, nitrification, denitrification, biogas production, sanitary landfill.
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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) | 0.000 | 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".