K<sub>2</sub>CO<sub>3</sub>-Catalyzed CO<sub>2</sub> Gasification of Ash-Free Coal: Kinetic Study
Bibliographic record
Abstract
The kinetics of K 2 CO 3 -catalyzed CO 2 gasification of ash-free coal was investigated with a thermogravimetric analyzer and compared to raw coal and uncatalyzed ash-free coal. At 750 °C, the gasification of ash-free coal dry mixed with 20 wt % K 2 CO 3 was approximately 3 and 60 times faster than the raw coal and ash-free coal without catalyst, respectively. Increasing the amount of catalyst from 20 to 45 wt % increased the gasification rate 3-fold. The gasification rate of ash-free coal containing potassium catalyst strongly depended upon the pretreatment (i.e., heating gas atmosphere and heating time) because it directly affected the degree of catalyst reduction. The catalytic gasification behavior could only be predicted with the extended random pore model, whereas the random pore model and integrated model were essentially equal for fitting the gasification rate for raw and ash-free coal. The activation energy for the catalyzed ash-free coal gasification was approximately 100 kJ mol –1 larger than for raw coal and the uncatalyzed ash-free coal. This increase might be due to the energy required for the potassium (i.e., catalyst) transfer to a new carbon site or caused by the pyrolysis process, because the formed char might have different properties.
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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".