Reducing the Environmental Impact of Fugitive Gas Emissions through Combustion in Diesel Engines
Bibliographic record
Abstract
Results of an experimental investigation into the extent of methane conversion when introduced in extremely small concentrations down to around a fraction of one percent by volume into the intake of a swirl chamber diesel engine are presented. Such an approach represents effectively an unconventional dual fuel engine operation with exceptionally lean gaseous fuel mixtures but combined with unusually very large diesel fuel pilot quantities. It is to be shown that for wide ranges of gas admission concentrations, diesel fuel quantity injected, load and speed that much of the methane added did get oxidized in the indirect injection engine to an extent from 53% to 80%, to appear in the form of carbon dioxide while contributing at the same time to the power output. Such methane admission tended to increase carbon monoxide exhaust emissions slightly, indicating that part of the methane may not have been fully oxidized. However, among the methane converted by engine combustion, about 80% is converted completely to carbon dioxide. It is suggested that such an approach converts the highly strong greenhouse gas methane into carbon dioxide, a much weaker greenhouse gas. Moreover, such an approach appears more effective than attempts made to dispense of any fugitive methane gas discharges into the compressor intake air of an industrial gas turbine. The implication of such performance results to dual fuel engine combustion is also highlighted.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".