Effects of the surface properties and particle size of hydrated lime on desulfurization
Bibliographic record
Abstract
In the gas treatment center in smelters, hydrogen fluoride (HF) is separated from the outlet gases of electrolysis cells, which are used to produce aluminum from alumina. However, SO 2 largely remains in the effluent gas. Another method has to be developed to separate this gas which is harmful to the environment. In this study, semi-dry desulfurization of a SO 2 containing gas was performed at low SO 2 concentrations using hydrated lime [Ca(OH) 2 ] as a catalytic desulfurizer under specific humidity conditions. The low reaction temperature of 100 °C and minimal use of the Ca-based desulfurizer under 17 % relative humidity achieved more than 95 % removal of SO 2 . The morphological changes and presence of sulfur in different lime samples were analyzed by scanning electron microscopy and energy-dispersive X-ray spectroscopy. Brunauer–Emmett–Teller (BET) analysis showed changes in the surface properties of hydrated lime after desulfurization. X-ray photoelectron spectroscopy (XPS) analysis provided the phase and composition identification of the sulfur species on hydrated lime and the CaSO 3 /CaSO 4 product ratio. Based on the experimental results, the optimum catalyst surface area with a specific particle size is critical to the effective conversion of Ca(OH) 2 into CaSO 3 and CaSO 4 . The practicality of a Ca-based desulfurizer and its ability to convert into the required product may be the key to reducing the overall cost of desulfurization in aluminum industry.
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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 | 0.001 |
| 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".