Screening of Binders for Pelletization of CaO-Based Sorbents for CO<sub>2</sub>Capture<sup>†</sup>
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Bibliographic record
Abstract
CaO-based CO 2 looping cycle technology is a promising method for separation of CO 2 from flue gas and syngas at high temperatures. The process of CO 2 capture is expected to take place in fluidized-bed combustion (FBC) systems, which implies significant attrition and elutriation of the solid sorbent. Hence, both reactivation of spent sorbent and preparation of modified CaO-based sorbent may be required to maximize the performance of the sorbent. One of the more promising methods to achieve reactivation, namely, hydration, seems to produce very fragile particles, which are unlikely to be suitable for FBC applications. Thus, it is expected that pelletization of the obtained powder may be required. In this paper, we present initial results on the screening of suitable binders for pelletization. Two types of bentonite (Na- and Ca-bentonite) and four types of commercial calcium aluminate cements (CA-14, CA-25, Secar 51, and Secar 80) were investigated here, with a primary focus of maintaining a high CO 2 -capture capacity over 30−35 cycles. The tests were carried out using a thermogravimetric analyzer (TGA), and the results showed that the presence of bentonites led to faster decay in activity as a result of the formation of calcium-silica compounds with low melting points, which leads to enhanced sintering. This is confirmed by scanning electron microscopy (SEM) and also X-ray diffraction (XRD), which showed the presence of spurrite [Ca 5 (SiO 4 ) 2 CO 3 ] as the dominant compound in the pellet after this series of cycles. Better results were obtained with no binder, i.e., by hydration of lime, where Ca(OH) 2 plays the role of the binder. Promising results were obtained also with calcium aluminate cements, where no effect of sintering because of the presence of these binders was noticed. Thus, on the basis of this study, the use of calcium aluminate cements for pelletization of CaO-based sorbent is recommended.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it