Reactivation of Fluidized Bed Combustor Ashes: Economic Evaluation and Implementation
Bibliographic record
Abstract
When high-sulfur-content coal or coke is used as fuel in fluidized bed combustors, a large excess of limestone or dolomite must be added for good SOx capture. All of the limestone is calcined but only 30–40% is actually sulfated. The resultant ashes are difficult to dispose of because of the free calcium oxide. These ashes can be reactivated for further SOx capture. A proposed, economic process involves wet grinding of the ashes with sufficient excess water to allow both complete hydration and good grinding conditions. To prevent cementitious solidification of the wet product, it is then mixed with selected dry materials, for example fine coal, to absorb the excess water. Wet waste coal fines or sludges may also be used, then both to provide the water and prevent solidification. The product is then granulated with the cementitious reactions providing a binder for the granules. Good results with large additional SOx capture have been observed both in a small pilot-sized CFBC and during a 54-hour utility boiler test in a 35 MWt boiler. Calcium utilization was nearly doubled, with significant reduction of CO2 emissions. Based on the test results, quick equity payback is expected with savings from reduced limestone purchase and ash disposal costs. In collaboration with The Babcock and Wilcox Company (B&W), a long test program at the Southern Illinois University in Carbondale, IL is planned.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".