Advanced oxidation processes for treating raw leachate: an experimental study (Barka landfill, Oman)
Bibliographic record
Abstract
Management of municipal solid waste leachate has become an urgent environmental problem in many countries due to the high growth of population and consumption trends. Advanced oxidation processes (AOPs) are efficient methods to treat leachate because of their high ability to reduce unpleasant parameters. Although the simultaneous application of different AOP methods is more effective than a single process, the simultaneous application of AOPs for the treatment of municipal solid waste leachate has received little attention. To fill this knowledge gap, this study presents the simultaneous application of AOP methods to treat municipal solid waste leachate. The efficiency of AOPs was studied in terms of organic compounds such as chemical oxygen demand (COD) and total organic carbon (TOC). Leachate samples were collected from the Barka landfill, which is located in the north-east of Oman. Four combinations of applied AOP methods (e.g. ozone (O3), hydrogen peroxide (H2O2) and ultraviolet (UV)) in this study resulted in the order of hydrogen peroxide/ozone/UV > hydrogen peroxide/ozone > ozone/UV > hydrogen peroxide/UV according to the COD removal efficiency. Combining these AOP methods resulted in an about 1.5 times increase in COD removal (56.62%) and a 1.9 times increase in TOC removal (39.34%) compared with the individual applications.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".