The Behavior of Free Lime in CFBC Ashes
Bibliographic record
Abstract
Ash disposal and ash use are critical to FBC technology and in turn the reactions of FBC ash with water are key to both of these two issues. An effective ash reactivation technology would improve the economics for FBC firing of high-sulphur fuels. Similarly, controlled ash hydration before disposal is the standard method for conditioning FBC ashes when firing high-sulphur fuels with limestone addition. Ashes can be hydrated with liquid water or by steam under pressure and our earlier work suggested that when FBC ashes were hydrated by either method, the components derived from the coal and those from the sorbent can interact chemically. As a result, the amount of “free CaO” (defined as the proportion of CaO and Ca(OH)2, expressed as CaO) may change. Usually, “free CaO” increases after hydration, particularly under pressure. However, there is also evidence that some of the CaO, derived from excess limestone sorbent, enters into reaction with the ash components, possibly silica or silicates. Such processes must modify the exothermicity of the ashes with water and affect their subsequent behaviour. This implies that it is incorrect to assume, as has often been done, that the heat of the hydration process is directly proportional to the CaO content of the ash. Furthermore, the results presented here also strongly support the view that one must include these interactions when looking at FBC hydration.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".