Natural gas spark ignition engine efficiency and NO <i> <sub>x</sub> </i> emission improvement using extreme exhaust gas recirculation enabled by partial reforming
Bibliographic record
Abstract
Natural-gas (NG), spark ignition (SI) engines have widespread application in the power generation and upstream oil and gas industries. The manufacturers of these engines are being challenged to meet increasingly stringent nitrogen oxide (NO x ) emission regulations without sacrificing fuel conversion efficiency. SI engines may be operated with air—fuel mixtures lean of stoichiometric to achieve higher thermal efficiency and to reduce NO x emissions. Compared with new combustion strategies such as homogeneous charge compression ignition, however, lean SI combustion suffers from a somewhat limited tolerability to mixture dilution and high cyclic variations. Alternatively, NO x emissions may be reduced by using exhaust gas recirculation (EGR) to dilute a stoichiometric air—fuel mixture. This paper investigates the application of reformer gas (RG) to enable a higher mixture dilution of an NG SI engine using EGR. It was found that RG enrichment allows an increase in the EGR dilution of a stoichiometric NG—air mixture from 12 per cent to more than 35 per cent. The optimal level of RG enrichment directly compensates for the combustion phasing retardation effect of EGR. Increasing the RG fraction in the mixture beyond the optimal value adversely affected the combustion process and fuel conversion efficiency. The experimental data suggest that NO x emissions comparable with the forthcoming 2010 US Environmental Protection Agency heavy-duty diesel engine regulations may be achieved using EGR, RG enrichment, and a three-way catalytic converter (TWC). An energy balance showed that there is the potential to increase the overall system fuel conversion efficiency slightly owing to a more optimized combustion process after taking into account the energy losses associated with fuel reforming. The approach of EGR, fuel reforming, and a TWC is suitable for retrofits because it can be accomplished without modifying the engine geometry.
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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) | 0.001 | 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".