CFD <b>s</b>tudy on partial oxidation of methane in fixed‐bed reactor
Bibliographic record
Abstract
Abstract Partial oxidation of methane (POM) is a preferred method for synthesis gas, which usually occurs in fixed bed reactors. In this paper, the discrete element method (DEM) is used to reconstruct the structure of a reactor bed via simulating the process of filling the reactor with catalyst. The particle resolved CFD physical model with the detailed micro‐kinetcis of the POM reaction was established to study the interaction among reactant flow, heat and mass transfer, and reaction in the fixed bed. The gas composition and temperature distribution in the reactor were obtained based on the simulation results. The effects of the space velocity and the reaction temperature on the CH4 conversion, catalyst selectivity, and catalyst surface coke formation were analyzed. The simulation results show that the temperature hot spots of the catalyst in the bed occur at the inlet and the temperature increases further near the wall. With the increase in space velocity, the conversion rate of CH4 decreases gradually, and the selectivity does not change significantly. As the temperature increases, the conversion rate of CH4 gradually increases and the selectivity decreases. The risk of coke formation on the catalyst surface rises axially and the C species concentration is relatively higher near the outlet. Appropriately increasing the gas velocity and increasing the temperature helps to reduce the surface coke accumulation of the catalyst.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".