Numerical Investigation of the Effects of Coke on Transport Properties in an Oxidative Fuel Cell Reformer
Bibliographic record
Abstract
Abstract Experimental investigations on the technical viability of solid oxide fuel cells to replace internal combustion engines in automobiles have increased in recent years. However, the performance and stability of catalysts in the presence of carbon is key for the commercial success of fuel cell reformers. In this paper, finite element method was used to study the effect of coke deposition on heat and mass transfer during the catalytic partial oxidation of ethanol in a packed bed reactor. The properties of Ni/Al2O3 catalyst bed were investigated after being subjected to several hours of carbon buildup. Bed permeability, porosity, and temperature distribution were significantly affected after just 1500 s of reaction time. It was observed that void fraction and permeability became nonuniform across the bed. These two parameters decreased with axial position, and the difference became more pronounced with time. A decrease in bed porosity reduced the bed temperature due to an increase in effective thermal conductivity and ethanol conversion and hydrogen selectivity decreased as a result. Thus, it was concluded that heat transfer becomes a limiting factor in reforming reactions in the presence of carbon. Production distribution before deactivation was also studied, and it was observed that a maximum ethanol conversion of 100% was achieved at 600 °C and a C/O ratio of 1.0. Finally, results from the reactions were compared to that of a different study to validate the reaction mechanism and similar results were found in the literature.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".