Attrition of Calcining Limestones in Circulating Fluidized-Bed Systems
Bibliographic record
Abstract
Limestone attrition in circulating fluidized-bed combustors (CFBCs) has received limited attention. Although there are a number of early studies on attrition in bubbling-bed systems, most current studies focus on simultaneous calcination and sulfation. However, this subject is increasing in importance as CO 2 looping cycles are proposed. CO 2 looping cycles involve repeatedly calcining the CaCO 3 component of the limestone to drive off a pure stream of CO 2 for storage or sequestration. Here, we have looked at five limestones from across Canada, the United States, and Mexico to determine the extent of their attrition under calcining conditions in fluidized-bed systems. This work shows that attrition varies very significantly from limestone to limestone, and even among different batches. It is clear, therefore, that each limestone will have to be carefully categorized to determine its potential for use in such cycles. Also, since limestones crush differently, even those limestones that are double-sieved may have very different initial size distributions. This will affect the results seen in tests carried out under realistic conditions. This work shows that most of the material loss in multiple calcination/carbonation cycles is in the first few cycles, and that even a very low level of sulfation can be a very effective means of reducing that material loss.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".