Mercury removal from spent low‐level mercury catalyst by thermal treatment
Bibliographic record
Abstract
Abstract Spent low‐level mercury catalyst (SLMC) from the polyvinyl chloride industry is a mercury‐containing hazardous waste. Thermal treatment technology was used to detoxify SLMC and recover mercury in this paper. Effects of flow rate of nitrogen, treatment temperature, and time on mercury removal efficiency were estimated 99.91% of total mercury was removed when SLMC was treated at 600°C for 30 min with 120 L/h of nitrogen. SLMC could be detoxified after being treated at 350°C for 120 min, 400°C for 60 min, or 450°C for 10 min based on the TCLP test. Migration rule of mercury during thermal treatment was analyzed by using improved sequential extraction procedure. Soluble and exchangeable mercury, and mercury combined with labile organics, were removed more easily than HgS and mercury combined with non‐labile organics. Removal process of total mercury followed a first‐order kinetic model. The experimental results may be useful for disposing of SLMC using thermal treatment in engineering practise.
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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) | 0.001 | 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".