<scp>CO<sub>2</sub></scp> gasification kinetics of Shenhua bituminous coal by isothermal thermogravimetric analysis
Bibliographic record
Abstract
Abstract Gasification kinetic analysis of three Shenhua bituminous coal samples with various particle size ranges were carried out at three different temperatures (1000, 950, and 900°C) for 30 min using the gasifying agent of CO2 with the flow rate of 40 ml min−1. The average particle sizes for sample A, sample B, and sample C were 43.4, 168, and 293 μm, respectively. Experimental results indicated that there were differences among the weight loss curves of three coal samples with different particle sizes. Furthermore, the differences became larger with the decrease of temperatures. Among the 11 used reaction mechanisms, two‐dimensional growth of nuclei following the Avrami‐Erofeev equation (A2) was proved to be the most appropriate one to fit the gasification data of Shenhua bituminous coal samples, as it can reconstruct gasification curves with high correlation coefficients (R2 > 0.95). The calculated values of apparent activation energy (E) for sample A, sample B, and sample C were 95.9, 79.1, and 69.4 kJ mol−1, respectively. It was also found that the particle size had significant influence on kinetic data. The apparent activation energy decreased when there was an increase of the particle size. The compensation relationship of E and A (apparent pre‐exponential factor) was noted, and the fitted mathematic formula was lnA = 0.1041 E+0.540 28 (R2 = 0.999).
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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".