Experimental Analysis of a Sabatier Reactor for Renewable Natural Gas Generation from Biogas: Ignition, Parameter Sensitivity Analysis, and Stability
Bibliographic record
Abstract
Biogas is a product of anaerobic fermentation, which is rich in CO 2 . The upgrade of biogas to renewable natural gas (RNG) is commercially achieved by separating carbon dioxide (CO 2 ) and impurities to improve its quality. As an alternative, the CO 2 contained in biogas can be directly converted into CH 4 via the thermocatalytic Sabatier reaction without separation, using H 2 generated by water electrolysis (utilizing renewable or surplus, low-carbon-footprint electricity). One of the major elements of this technology is the configuration of the Sabatier reactor. For industrial applications, it is beneficial to eliminate the energy-intensive CO 2 separation step, converting biogas to RNG directly. In this study, we report the experimental lab-scale proof of concept of the autothermal Sabatier reactor for direct biogas upgrade. We demonstrate a completely autothermal operation of the air-cooled, stainless steel reactor using a commercial Ni catalyst with a synthetic biogas feed. The effects of feed temperature, space velocity, and reactor cooling were investigated using three prototypes with different sizes and configurations. The maximum CO 2 conversion of 91% with 100% selectivity to CH 4 generation was achieved in a 10″-length reactor, over 100 h of continuous, stable operation, without any external reactor heating or feed preheating.
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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 | 0.001 | 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 | 0.000 |
| 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".