STUDY OF CO2 CAPTURE USING CO2 LOOPING COMBUSTION TECHNOLOGY
Bibliographic record
Abstract
Absorption processes for CO2 separation employing solid sorbents such as limestone appear to be technically feasible and cost effective. At appropriate temperature and pressure, CO2 from flue gas stream is absorbed by CaO-based sorbent via the carbonation reaction, and the sorbent is then regenerated in a separate reactor and a nearly pure CO2 stream is produced suitable for industrial use or ultimate sequestration. This technology has now been demonstrated using the CANMET pilot-scale dual-fluidized bed system. Havelock limestone from eastern Canada, was used as the CO2 sorbent, while a synthesis gas mixture of air and CO2 (15％) was employed to represent combustion flue gas. Oxy-fuel combustion of biomass and coal was employed in the sorbent regenerating step, creating a high CO2 concentration off-gas stream suitable for sequestration. Pure O2 was mixed with recycled off-gas and this along with the excellent heat transfer characteristics of fluidized bed allowed us to use an O2 ratio of 40％ in the combustion gas. In addition, samples were characterized for pore distribution (nitrogen adsorption/desorption: BET and BJH) and skeleton characterization (density by He pycnometry), as well as changes in sample volume during hydration (sample swelling). The results obtained showed successful hydration even for hydration periods as short as 15 min, and very favorable sample properties. Their pore surface area, pore volume distribution and swelling during hydration are very promising with regard to their use in additional CO2 capture cycles or SO2 retention.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".