Combustion characteristics of ultrafine gasified semi‐char in circulating fluidized bed
Bibliographic record
Abstract
Coal fluidized bed gasification generates large amounts of semi‐char, which has a high carbon content. Effectively re‐burning semi‐char has become a promising way to improve overall conversion efficiency of coal gasification and use the residue carbon in the semi‐char. However, due to the ultrafine particle size (mean particle size ≈ tens of microns), nearly zero volatile content, and the graphitization structure of the semi‐char, the fluidization and combustion organization of semi‐char in a circulating fluidized bed (CFB) is difficult, leading to poor self‐sustained combustion stability and low combustion efficiency. The physicochemical properties of the semi‐char from an industrial CFB gasifier were studied. The combustion characteristics and activation energy of the semi‐char were tested using thermogravimetric analysis. The occurrence of sulphur and nitrogen in the semi‐char and fly ash after combustion were characterized with X‐ray photoelectron spectroscopy (XPS). A re‐burning experiment was carried out in a 5 t/day CFB pilot facility. The key is to build a balance between sufficient residence time to burnout and a proper fluidization velocity for bed material cycle. The effects of residence time, preheating temperature, and the ratio of preheating air to fuel on the combustion efficiency and pollutant emission was studied. The balance between the preheating time and residence time in the furnace was discussed. Results show that it can reach stable combustion in a specially designed CFB furnace without auxiliary fuel and the combustion efficiency can be higher than 98 %.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".