Long-Term Calcination/Carbonation Cycling and Thermal Pretreatment for CO<sub>2</sub> Capture by Limestone and Dolomite
Bibliographic record
Abstract
Capturing carbon dioxide is vital for the future of climate-friendly combustion, gasification, and steam-re-forming processes. Dry processes utilizing simple sorbents have great potential in this regard. Long-term calcination/carbonation cycling was carried out in an atmospheric-pressure thermogravimetric reactor. Although dolomite gave better capture than limestone for a limited number of cycles, the advantage declined over many cycles. Under some circumstances, decreasing the carbonation temperature increased the rate of reaction because of the interaction between equilibrium and kinetic factors. Limestone and dolomite, after being pretreated thermally at high temperatures (1000 or 1100 °C), showed a substantial increase in calcium utilization over many calcination/carbonation cycles. Lengthening the pretreatment interval resulted in greater improvement. However, attrition was significantly greater for the pretreated sorbents. Greatly extending the duration of carbonation during one cycle was found to be capable of restoring the CO 2 capture ability of sorbents to their original behavior, offering a possible means of countering the long-term degradation of calcium sorbents for dry capture of carbon dioxide.
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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".