Application of Spent H<sub>2</sub>S Scavenger of Iron Oxide in Mercury Capture from Flue Gas
Bibliographic record
Abstract
A commercial H 2 S scavenger of iron-oxide base, namely TG-4 in the Chinese market, was found to have activity for mercury (Hg) capture from flue gas after its use in H 2 S removal. The technical feasibility of using this industrial spent material to replace expensive activated carbon for mercury capture from coal-fired power plant flue gas was studied in this paper. The effects of temperature, space velocity, and compositions of the flue gas on the efficiency of Hg removal was investigated using a packed-bed tubular reactor on the benchtop scale. The mercury sorbents prepared from the spent TG-4 H 2 S sorbent were characterized by sulfur measurement, thermogravimetric and differential thermal analysis (TG-DTA), and X-ray absorption spectroscopy (XAS). The Hg L 3 -edge XAS was used to analyze the material after its contact with Hg. The results show that the spent TG-4 can efficiently remove elemental mercury from the gas at a temperature range between 80 and 240 °C and at reasonable space velocity. The efficiency of Hg removal slightly decreased when acidic gases such as SO 2, NO, and HCl were present in the gas stream. HgS was observed in the adsorbent after its reaction with gases containing Hg vapor, indicating that the elemental sulfur was the active component for mercury capture. However, compared with the benchtop experimental results, the spent TG-4 gave lower Hg capture efficiency in the scaled-up test at SaskPower’s Emission Control Research Facility.
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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".