Reaction–Diffusion Model for Gasification of a Shrinking Single Carbon-Anode Particle
Bibliographic record
Abstract
Abstract The present work focuses on the gasification of a single carbon-anode particle with CO2, using a detailed reaction-transport model based on the reaction intrinsic kinetics and transport of gaseous species. The model includes the mass conservation equations for the gas components and solid carbon particles, resulting in a set of nonlinear partial differential equations, being solved using numerical techniques. The model may predict the gas generation rate, the gas compositions, and the carbon consumption rate during the gasification of a carbon particle. Five kinetic models were compared to describe the gasification behavior of carbon particles. It was found that the random pore model (RPM) provided the best description of the reactivity of anode particles. The model also predicted the particle shrinkage during the gasification process. The model was validated using experimental results obtained with different particle size ranges, being gasified with CO2 at 1233 K. The experiments were performed in a thermogravimetric analyzer (TGA). Good agreement between the model results and the experimental data showed that this approach could quantify with success the gasification kinetics and the gas distribution within the anode particle. In addition, the Langmuir–Hinshelwood (L–H) model is used in order to capture the inhibition effect of carbon monoxide on the gasification reaction. The effectiveness factor and Thiele modulus simulated for various particle sizes helped assess the evolution of the relative dominance of diffusion and chemical reactions during the gasification process.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".