Effect of coal particle size on gasification performance of two‐stage entrained‐flow coal gasifier
Bibliographic record
Abstract
Abstract To clarify the effect of coal particle sizes on gasification performance of an advanced two‐stage entrained‐flow coal gasifier for IGCC (integrated gasification combined cycle) application, a comprehensive three‐dimensional numerical model is established by incorporating the shrinking core model combined with the Langmuir‐Hinshelwood kinetic rate expression, which considers the inhibitory effect of CO on char‐CO 2 reaction. The flow, temperature, and species distributions were obtained, and the results are consistent with the operating data. Results show that the helical flow and eight recirculation zones in the gasifier improve carbon conversion efficiency through extending the residence time of coal particles. Slow devolatilization of large particles caused by slow heating retards volatiles combustion, and thus char combustion and gasification. As a result, less char gasification and higher gas temperature appear in the injection region of the first stage. Higher inertia of larger particles produces higher slip velocity, which enhances heat transfer and mass diffusion to char particles and increases char consumption rate in diffusion‐limited regions. The regions of high inner wall temperature spread from locations around burners to the whole inner wall of the injection and bottom regions in the first stage with increase of coal particle sizes.
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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 | 0.000 | 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".