Reuse of Spent Sorbents from FBC for SO<sub>2</sub> Capture by Simultaneous Reactivation and Pelletization
Bibliographic record
Abstract
Sorbent utilization of CaO-based sorbents for in situ SO 2 capture in fluidized bed combustion (FBC) is far from quantitative, and in consequence FBC residues contain a significant amount of unreacted CaO. Treatment of bed materials by hydration with liquid water or steam can reactivate the spent sorbent for further SO 2 capture, although, to date, the costs of such processes have deterred its practical use. By contrast, fly ash is already very reactive, but given its short residence time in the combustor, direct reuse of fly ash appears to be an ineffective strategy. This is significant as fly ash often accounts for the majority of solid waste streams discharged from FBC systems. This paper describes a new technique for reactivation of FBC spent sorbent and preparation of pellets suitable for SO 2 capture, which can also incorporate the fly ash into the pellets so that it has an adequate residence time in the primary combustion loop of a CFB to realize improved sulfur capture. Reactivation and pelletization of the spent sorbent were achieved simultaneously in a mechanical pelletizer with the addition of spray water. Four types of pellets were prepared with various proportions of bed ash and fly ash. Quick lime (CaO) powders were also tested as a useful additive for the pelletization process. The effectiveness of the reactivation technique was tested by the nitrogen physisorption, which confirmed that a more suitable pore surface area and pore volume distribution for sulfation were developed. The SO 2 capture potential of the pellets was also examined in a thermogravimetric analyzer. The reactivated pelletized sorbents showed an improved sulfation rate in comparison to both the original sorbent and the spent sorbent, particularly during the diffusion-controlled reaction stage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".