CO<sub>2</sub> capture and attrition performance of competitive eco‐friendly calcium‐based pellets in fluidized bed
Bibliographic record
Abstract
Abstract A system incorporating spent bleaching clay (SBC) into the calcium looping (CaL) process has been proposed. In this paper, prepared sorbents doped with regenerated SBC and cement were tested in a bubbling fluidized bed (BFB) to examine in detail their cyclic CO2 capture capacity and attrition properties. The results revealed that the cyclic CO2 capture capacity of pellets modified by pyrolyzed SBC and/or cement showed significantly better performance than limestone, which is consistent with the thermogravimetric analyzer (TGA) results. This is due to the improvement of pore structure and enhanced sintering resistance created by adding support materials to the sorbent. The elutriation rates of the composites prepared with pyrolyzed SBC and/or cement were consistently lower than for crushed limestone. Scanning electron microscopy (SEM) images indicated that the pellets possessed higher sphericity than limestone particles, thus reducing surface abrasion. Limestone exhibited a high attrition rate (diameter reduction rate) of 10.7 μm/cycle, which could be eliminated effectively by adding regenerated SBC and/or cement. ‘L‐5PC‐10CA’ (85% lime/5% pyrolyzed SBC/10% cement) exhibited an attrition rate of only 7.9 μm/cycle. Based on the analysis of breakage and probability density function (PDF) for particle size distribution, it appeared that pellets without cement experienced breakage (mostly chipping and disintegration) and surface abrasion, whereas ‘L‐10CA’ (90% lime/10% cement) and ‘L‐5PC‐10CA’ mainly suffered surface abrasion, combined with some chipping. © 2018 Society of Chemical Industry and John Wiley & Sons, Ltd.
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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".