The Combustion and Emissions Performance of a Syngas-Diesel Dual Fuel Compression Ignition Engine
Bibliographic record
Abstract
Remote communities in Canada heavily rely on reciprocating diesel generators for heat and power generation. These engines utilize diesel fuel that is imported at great expense and generate green-house gas (GHG) and pollutant emissions. Replacing diesel fuel in these engines by syngas derived from a thermo-chemical treatment of local renewable biomass can not only lower the fuel cost but also reduce GHG and pollutant emissions for remote communities. Besides, syngas-diesel dual fuel combustion can maintain the ability to revert back to diesel operation and therefore ensure reliable heat and power supply when syngas is not available. In this study, the combustion and emissions performance of a syngas-diesel dual fuel engine was investigated at low and medium loads. A single cylinder direct injection diesel engine was modified to operate using a dual fuel strategy. The diesel fuel was directly injected to the cylinder, while syngas was injected into the intake port. The effects of syngas fraction and composition on energy efficiency, cylinder pressure, exhaust temperature, and combustion stability were recorded and analyzed. The emissions data, including PM, NOx, CO, and unburned hydrocarbon, were also analyzed and reported in the paper. The results suggest that the substitution of diesel by a syngas caused a slight decrease in brake thermal efficiency and an increase in CO emissions. The effect of a syngas on soot emissions depended on the composition and/or quality. The inert component content of a syngas significantly affected NOx emissions in a syngas-diesel dual fuel internal combustion engine.
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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".