Supercritical water oxidation treatment of humic acid as a model organic compound of landfill leachate
Bibliographic record
Abstract
Abstract Landfill leachate is a complex and variable effluent, rich in organic and inorganic matters resistant to decomposition, and is an extreme pollutant. Humic acids (HA) are some of the most refractive substances in the leachates, which is the reason why they have been used as an organic model. The degradation of an HA solution through supercritical water oxidation (ScWO) was evaluated under a constant pressure of 22.5 MPa, temperatures from 400°C to 600°C, and reaction times from 15 to 60 seconds. The results showed that the most influential factor was temperature and it guided the operational conditions of the ScWO for the landfill leachate (22.5 MPa, 600°C, and 60 seconds). The landfill leachate treatment promoted high removal rates of true colour (87%), total dissolved solids (94%), nitrate (70%), and total phosphorus (96%). In addition, it removed 57% of the COD, which was similar to the HA removal rate (61%) under the same operational conditions. This indicates that HA are an adequate organic model for landfill leachates. The results suggest that ScWO is a clean and promising treatment technology that can be applied to landfills and can have even better results if used in combination with oxidizers and catalysts, or with the use of higher temperatures.
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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".