Diesel direct injection and EGR optimization for a syngas-diesel dual-fuel generator operating at constant load
Bibliographic record
Abstract
Diesel generators are widely used to produce electricity and heat in remote communities. However, their use contributes to harmful pollutant and greenhouse gas (GHG) emissions, negatively impacting the environment and public health. Syngas, which is a sustainable fuel, can play an important role in the transition from petroleum to renewable fuels. When used in dual-fuel diesel engines, it can contribute to reducing GHG emissions and the cost of transporting diesel, especially for rural and remote communities. This study investigates the effects of optimizing the diesel direct injection (DI) and exhaust gas recirculation (EGR) strategies on the combustion and emission performance of a syngas-diesel dual-fuel generator at constant load. The experiments were conducted using a 30-kW generator with a four-stroke, four-cylinder, turbocharged, and electronically controlled direct-injection diesel engine. The intake manifold of the engine was modified to allow introducing syngas upstream of the turbocharger. The syngas was simulated using individually controlled flow rates of hydrogen (H2), carbon monoxide (CO), carbon dioxide (CO2), and nitrogen (N2). The syngas flow rate was adjusted to replace 40% of the energy provided by diesel fuel while optimizing the diesel DI parameters such as the pilot injection timing, main injection timing and diesel direct injection rail pressure, as well as the EGR rate. The findings of this study reveal that optimizing diesel injection strategy and appropriately raising the EGR rate have a positive impact on the engine performance and emissions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".