Landfill leachate treatment by waste-derived activated carbon
Bibliographic record
Abstract
This research evaluated the use of spent coffee grounds (SCG) and oat hulls as precursors for activated carbon applied for organic matter removal from leachate, a pollutant wastewater produced by landfills. These precursors were selected based on their global commercial significance. The high demand for the beverage coffee is directly proportional to the generation of SCG as organic waste. Similarly, oat is a popular cereal consumed worldwide, and its industrial processing generates hulls as organic waste. The activated carbon samples were produced by chemical activation with phosphoric acid (H3PO4), using distinct impregnation ratios: 50 and 100 % for SCG, and 60 and 100 % for oat hulls; followed by pyrolysis in an inert atmosphere, at 350 and 500 °C. The feasibility of the tested precursors as adsorbents was initially assessed in experiments with synthetic leachate. Afterwards, the results from the initial tests were compared with those obtained in real leachate treatment. The studies described in this thesis showed that the impregnation ratios and pyrolysis temperatures interfered with the surface areas of the formed adsorbents, consequently affecting the organic matter removal from leachate. Both oat hulls and SCG were successfully recovered as activated carbon, and efficiently treated synthetic and real leachate, removing more than 90 % of the organic matter. Therefore, this study highly encourages the use of SCG and oat hulls as precursors for activated carbon production and their application to treat leachate.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".