Methane pyrolysis for hydrogen production: Modeling of soot deposition by computational fluid dynamics and experimental validation
Bibliographic record
Abstract
Methane pyrolysis is an attractive technology to reduce greenhouse gas emissions, since the carbonaceous part of the hydrocarbon is captured into a solid, easier to store than carbon dioxide. In this framework, computational fluid dynamics (CFD) is an excellent tool to simulate the catalyst deactivation and the complex phenomena arising on the carbon surface. We investigated thermal and catalytical pyrolysis of methane in a tubular quartz reactor with an internal diameter of 3.8 cm. We modeled soot deposition and added a new surface mass balance that parametrize bed porosity as time-dependent variable. Our model predicts occlusion (clogging) time for empty and packed bed reactor: operating at 1373 K, an empty bed clogs in 15 d, which is coherent with industrial operations. The model well predicts methane conversion in the empty reactor even after 24 h time on stream. When a packed bed is used, the experimental conversion follow the predicted activity decay, with deviations due to the size distribution of the carbon during experiments. Radiation is the main heat transfer mechanism (86 % of the total heat absorbed by the system), compared to the others involved in this system, i.e., conduction, which is more than 50 times smaller, natural convection, more than 18 times smaller, and forced convection, more than 300 times smaller.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".