Arsenic Removal from Arsenopyrite-Bearing Iron Ore and Arsenic Recovery from Dust Ash by Roasting Method
Bibliographic record
Abstract
In most cases, arsenic is an unfavorable element in metallurgical processes. The mechanism of arsenic removal was investigated through roasting experiments performed on arsenopyrite-bearing iron ore. Thermodynamic calculation of arsenic recovery was carried out by FactSage 7.0 software (Thermfact/CRCT, Montreal, Canada; GTT-Technologies, Ahern, Germany). Moreover, the arsenic residues in dust ash were recovered by roasting dust ash in a reducing atmosphere. Furthermore, the corresponding chemical properties of the roasted ore and dust ash were determined by X-ray diffraction, inductively coupled plasma atomic emission spectrometry, and scanning electron microscopy, coupled with energy-dispersive X-ray spectroscopy. The experimental results revealed that the arsenic in arsenopyrite-bearing iron ore can be removed in the form of As2O3(g) in an air or nitrogen atmosphere by a roasting method. The efficiency of arsenic removal through roasting in air was found to be less than that in nitrogen atmosphere. The method of roasting in a reducing atmosphere is feasible for arsenic recovery from dust ash. When the carbon mass ratio in dust ash is 1.83%, the arsenic removal products is almost volatilized and recovered in the form of As2O3(g).
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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".