Influence of Steam Injection during Calcination on the Reactivity of CaO-Based Sorbent for Carbon Capture
Bibliographic record
Abstract
Calcium looping is an emerging CO 2 capture technology based on cyclic calcination/carbonation reactions using calcium-based sorbents. Steam is typically present in flue/fuel gas streams from combustion or gasification and in the calciner used for sorbent regeneration. The effect of steam in the calciner on sorbent performance has received little attention in the literature. Here, experiments were conducted using a thermogravimetric analyzer (TGA) to determine the effect of steam injection during calcination on sorbent reactivity during carbonation. Two Canadian limestones, Cadomin and Havelock, were tested, and various levels of steam (up to 40%) were injected in the sorbent regeneration process for 15 calcination/carbonation cycles. All concentrations of steam examined were found to increase sorbent reactivity for carbonation for both sorbents. In these experiments, 15% steam concentration with calcination had the largest impact on carrying capacity for both sorbents. Steam changes the morphology of the sorbent while calcination is occurring, probably causing a shift from smaller to larger pores, resulting in a structure which increases carrying capacity. It was also demonstrated that steam addition produced a larger impact on sorbent reactivity for carbonation than for calcination.
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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".