Influence of process parameters on carbonation rate and conversion of steelmaking slags – Introduction of the ‘carbonation weathering rate’
Bibliographic record
Abstract
Abstract Alkaline industrial wastes are considered potential resources for the mitigation of CO2 emissions by simultaneously capturing and sequestering CO2 through mineralization. Mineralization safely and permanently stores CO2 through its reaction with alkaline earth metals. These elements are found in a variety of abundantly available industrial wastes that have high reactivity with CO2, and that are generated close to the emission point‐sources. Among all suitable industrial wastes, steelmaking slag has been deemed the most promising given its high CO2 uptake potential. In this paper, we review recent publications related to the influence of process parameters on the carbonation rate and conversion extent of steelmaking slags, comparing and analyzing them in order to define the present state of the art. Furthermore, the maximum conversions resulting from different studies are directly compared using a new index, the Carbonation Weathering Rate (CWR), which normalizes the results based on particle size and reaction duration. To date, the carbonation of Basic Oxygen Furnace steelmaking slag, under mild conditions, presents both the highest carbonation conversion and CWR, with values equal to 93.5% and 0.62 μm/min, respectively. © 2016 Society of Chemical Industry and John Wiley & Sons, Ltd
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".