Combustion and emission performance of a syngas-diesel dual-fuel generator under constant load
Bibliographic record
Abstract
Remote communities in Canada still rely on diesel generators for their heating and electricity. Diesel combustion produces emissions of greenhouse gases (GHG) and other hazardous pollutants such as nitrogen oxides (NOx), carbon monoxide (CO), unburned hydrocarbons (HC) and particulate matter (PM) which affect negatively the environment and human health. Governmental regulations have forced manufacturers of fossil fuel powered systems to reduce emissions. Fuel switching from petroleum diesel to sustainable fuels such as syngas, which is a gaseous fuel that can be produced from local biomass through gasification process, has the potential to substitute diesel fuel in numerous engineering power systems and consequently reduce undesirable emissions. For instance, syngas can substitute a large quantity of diesel fuel in dual-fuel diesel engines and hence significantly reduce emissions. With a self-ignition temperature typically exceeding 500°C, sole syngas cannot be used in compression ignition (CI) engines. However, the utilization of syngas in CI engines can be achieved through dual-fuel combustion mode where only a small amount of diesel is needed to initiate ignition. The primary objective of this thesis is to thoroughly investigate the combustion and emission performance of a four-cylinder syngas-diesel dual-fuel generator under constant load conditions. This is achieved by varying syngas flow rates and composition, and optimizing direct injection (DI) strategies and exhaust gas recirculation (EGR) rate. The unique aspect of this research is that experiments were performed on a modern multi-cylinder diesel-fueled generator with minimal modifications, making it more relevant for practical applications. The findings from this study can potentially be directly applied to real-world scenarios, where existing diesel generators can be retrofitted to accommodate syngas utilization in dual-fuel combustion mode.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".