Scale-up challenges and opportunities for carbon capture by oxy-fuel circulating fluidized beds
Bibliographic record
Abstract
Oxy-fuel combustion is a promising technology for carbon capture and storage (CCS) from large point sources. In particular, fluidized bed (FB) boilers represent one of the power generation technologies capable of utilizing the oxy-fuel concept. This paper reviews the published material on the key aspects of oxy-fuel circulating FB, including the boiler heat balance, heat transfer mechanisms, furnace hydrodynamics, and the mechanical and chemical mechanisms of the process. In particular, it demonstrates the challenges of utilizing high inlet O2 concentrations in the oxy-fuel process in fluidized beds. This requires significantly more efficient gas-particle clean-up technology (especially for Cl with perhaps 19% retention and Hg with 2.15 μg/m3 in flue gases), high circulating solids flux and, hence, significant heat extraction outside the furnace (up to 60% of the boiler’s total heat extraction). Scale-up of oxy-fuel CFB technology can partially compensate for the energy penalty from air separation by furnace downsizing when operating at high inlet O2 concentrations. Critically, while there are numerous measurement campaigns and corresponding models from the pilot and, to a lesser extent, industrial scale, the paper endeavors to answer the questions about what information taken from such experimental campaigns is reliable, useful for future design, and for scale-up.
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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 | 0.002 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".