Decomposition of High Organic and Moisture Content Municipal Solid Waste in Bioreactor Landfills
Bibliographic record
Abstract
With the increase in waste recycling, municipal solid waste (MSW) with high organic and moisture contents are found in various landfills worldwide. If this kind of waste were put into anaerobic condition directly, the pH values will decrease sharply, which will seriously affect the biodegradation of the wastes. This study is aimed to investigate the decomposition of the type of MSW in aerobic condition before the anaerobic biodegradation. In the study, the effects of air addition and biosolids addition on the biodegradation of MSW with high organic and moisture contents were examined. Moreover, the flushing technology was compared with the leachate recirculation technology. Six simulated bioreactor landfills were set up. After about 100 days' operation, it was observed that (1) the mass reduction rate in the aerobic-anaerobic bioreactor was approximately five times of that in the anaerobic bioreactor, the leachate quality was much better than that in the anaerobic bioreactor based on the final COD, BOD 5, TS, and NH 3 concentrations. (2) biosolids have strong buffering effects and the addition of biosolids accelerated the anaerobic biodegradation progress to a great extent. Therefore, it was concluded that initially degrading MSW under aerobic condition before anaerobic degradation with biosolids addition is the optimum strategy for the decomposition of MSW with high organic and moisture contents.
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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".