Thermal plasma treatment of stormwater sediments: comparison between DC non-transferred and partially transferred arc plasma
Bibliographic record
Abstract
The disposal of enormous amount of stormwater sediments becomes an emerging worldwide problem. Stormwater sediments are contaminated by heavy metals, phosphorus, trace organic and hydrocarbons, and cannot be disposed without treatment. Thermal plasma decontamination technology offers a high decomposition rate in a wide range of toxic organic compound and immobilization of heavy metal. In this study, we compared the treatment results between two different modes of thermal plasma: (1) a non-transferred direct current (DC) mode and (2) a partial DC-transferred mode. The reductions of total organic carbon (TOC) were, respectively, 25% and 80% for non-transferred and partially transferred plasma, respectively. Most of the toxic organic compounds were converted majorly to CxHy. In the gaseous emission, the accumulated CxHy, CO, NO and H2S were significantly higher in partially transferred mode than in non-transferred mode. The solid analysis demonstrated that the concentrations of Ca and Fe were enriched by 500% and 40%, respectively. New chemical compositions such as KAlSi3O8, Fe3O4, NaCl and CaSO4 were formed after treatment in partially DC-transferred mode. The power inputs were 1 and 10 kW, respectively, for non-transferred DC mode and a partially DC-transferred mode. With a lower energy input, non-transferred plasma treatment can be used for decontamination of sediments with low TOC and metal concentration. Meanwhile, partially transferred thermal plasma with higher energy input is suitable for treating sediments with high TOC percentage and volatile metal concentration. The organic compounds are converted into valuable gaseous products which can be recycled as an energy source.
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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".