Advanced treatment of landfill leachate from a sequencing batch reactor (SBR) by electrochemical oxidation process
Bibliographic record
Abstract
Biologically stabilized landfill leachate usually requires further removal of organic substances and ammonia nitrogen (NH3-N) before final discharge. In this paper, the advanced treatment of landfill leachate pretreated by sequencing batch reactor (SBR) via electrochemical oxidation was carried out in an electrochemical compartment reactor with oxide-coated titanium anode (Ti/TiO2-IrO2). The effect of voltage, chlorine content, initial pH value, and ferrous sulfate (FeSO4) on the removal efficiency of contaminants was investigated systematically. The removal efficiency of organic pollutants and ammonia nitrogen (NH3-N) increased with the increase of voltage, chlorine content and the addition of ferrous iron. The initial pH value has a different effect on the removal of COD and NH3-N. Compared with the traditional electrochemical oxidation, the process with Fe(II/III) is more efficient with low power consumption, and the removal efficiency of organic pollutant is slightly lower when Fe(II) is used. A lower power consumption of 16.6 kWh·(kg COD)–1 indicated the electrochemical oxidation with Fe(II) was a promising alternative for the advanced treatment of 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) | 0.001 | 0.001 |
| 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".