Mercury Speciation under Laboratory Scale Direct Iron Ore Reduction Process Conditions
Bibliographic record
Abstract
Mercury is among the most toxic trace metals, a potential neurotoxin which bioaccumulates in the aquatic biota and subsequently enters the food chain. While mercury emissions from coal fired power stations have been extensively studied, much less effort has been devoted to characterise emissions of mercury from ironmaking process, where contributions of mercury present in the ore and in the coal used as a reducing agent may be significant. Understanding of the detailed chemistry of mercury in ironmaking systems and release of different forms of mercury to the atmosphere is important given that the distribution, mobility and bioavailability of mercury depends on its various chemical forms and oxidation states (or speciation). Moreover, speciation of mercury determines the extent of its capture in existing pollution control technologies. This study describes measurements of speciation of mercury in off gas from laboratory scale direct iron ore reduction process involving the use of circulating fluidised bed (CFB) reactor. Speciation of mercury in off gas was determined using the Ontario Hydro sampling train method. Samples of feed coal, iron ore and waste products were also collected during the experiments and were analysed for total mercury content to calculate the mass balance of mercury. The measurements were performed under several different operating conditions. An attempt was made to derive possible mechanistic understanding of the chemical reactions leading to mercury transformation under the reducing conditions of ironmaking processes.
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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".