Experimental Study of Oxy-Fuel Combustion and Sulfur Capture in a Mini-CFBC
Bibliographic record
Abstract
Oxy-fuel technology uses effectively pure oxygen for fossil fuel combustion in order to obtain a highly concentrated CO 2 stream, suitable for direct compression and sequestration. It is an effective technology to reduce greenhouse gas emissions to the atmosphere from large point sources such as power generation plants. Oxy-fuel FBC technology has the combined advantage of producing high CO 2 concentration flue gas and allowing excellent fuel flexibility. In addition, with external cooling of the recirculated solids, the flue gas recirculation ratio can be reduced. CETC-Ottawa has carried out oxy-fuel fluidized bed combustion with flue gas recirculation on its modified mini-CFBC. The mini-CFBC has an internal diameter of 100 mm and internal height of 5000 mm. Both bituminous and sub-bituminous coals were fired. Limestone was premixed with coal and fed to the mini-CFBC. Recirculated solids were cooled in the return leg of the mini-CFBC. The bed temperature was controlled at about 850 °C, while the oxygen concentration in the primary gas was ∼25% and in the secondary gas was ∼50%. With flue gas recycle, the CO 2 concentration in the flue gas reached 82–90%. Sulfur capture efficiency and CO and NO x concentrations were also measured and were all at acceptable levels. The transition from air firing to oxy-fuel firing was a fast and relatively smooth process, and operation of the mini-CFBC under oxy-fuel firing conditions was similar to that of air firing.
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 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".