Using the analytical heirarchy process to select specific methanation catalysts based on their extraction impacts
Bibliographic record
Abstract
Summary Methanation is an exothermic process that utilizes catalysts to convert the carbon dioxide and carbon monoxide in biogas to methane, forming synthetic natural gas. The criteria for determining a suitable catalyst should not only be its effectiveness, but also the environmental impact of extracting and refining the metal. The authors examine the environmental impacts of implementing a select group of methanation catalysts in the field for industrial scale synthetic natural gas production using the analytical hierarchy process (AHP). Catalysts containing a combination of rare earth metals are investigated separately and AHP is used to rank the catalysts based on their environmental impact per kg of CO2 converted. It is determined that catalysts containing common metals, such as nickel, have the lowest environmental impact per conversion rate across a number of metrics and represented the catalysts with the second and third highest conversion rates analyzed. Catalysts containing ruthenium are found to be the most detrimental to the environment, in spite of the favorable conversion rate offered by a ruthenium‐cesium catalyst in methanation reactors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".