Modelling and experimental validation of a CO<sub>2</sub> methanation annular cooled fixed‐bed reactor exchanger
Bibliographic record
Abstract
Abstract A simulation model of a fixed‐bed reactor‐exchanger dedicated to CO2 methanation on an industrial Ni/γ‐Al2O3 catalyst has been built on the basis of experimental characterization of heat transfer and kinetic parameters. An effective thermal conductivity of the bed and a wall heat transfer coefficient are determined from cooling experiments of different Ar‐H2 mixtures (thermal conductivity 0.02–0.25 W · m−1 · K−1) at different Reynolds numbers (particle Reynolds number 1–50). The flow dependent component of the Nusselt number correlates to the gas Prandtl number as Pr0.72. These heat transfer parameters and a kinetic model adapted to the Ni/γ‐Al2O3 catalyst are integrated in mass, heat, and momentum balance equations in the bed and at the particle scale to build a 2D heterogeneous model of the fixed‐bed reactor. CO2 methanation experiments in an annular fixed‐bed reactor‐exchanger filled with 400 g of Ni/γ‐Al2O3 catalyst at pressures from 0.4 to 0.8 MPa and coolant temperatures from 473 to 548 K (200 to 275 °C) are described in this paper and simulated by the model. CO2 conversion rate and CH4 selectivity at the reactor outlet and temperature elevations in the reactor are simulated by the model with a discrepancy lower than 10 %. For pressures above 0.4 MPa, a strong mass diffusion limitation inside the catalyst particles is shown and the efficiency decrease of the three reactions is explained.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".