Modelling methane and ethane photolysis in waste gas: Optimization of reaction networks
Bibliographic record
Abstract
Abstract A mechanistic model for the ultraviolet degradation of methane and ethane in waste gas was developed with the focus on reaction network development and optimization. The research serves a dual purpose of removing natural gas condensate emissions, and converting condensates to added‐value products. A comprehensive reaction network including all possible reactions was developed, and kinetic constants were taken from the literature or estimated based on analogues. Overall, 162 reactions were included for the most complete case. Next, the model was screened for reactions that did not affect the simulation results. As complexity increases, it was shown that the model devotes an increasing fraction of time calculating reactions that are negligible in the overall process. Based on the simulation results it is expected that the model can be extended to higher alkanes with a reasonable run‐time in addition to keeping precise simulation results. The model predicts that high ethane conversion is possible while maintaining low methane conversion, and it is expected that this effect will be even more pronounced in the presence of higher alkanes. Overall, the projected model can be used to establish the feasibility of converting natural gas condensates to value‐added products without degrading its main content, methane; provided that oxygen and water vapour are available for the oxidation.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".