Effect of Engine Speed and Biogas Composition on Performance of a Small Biogas-Diesel Dual-Fuel Generator
Bibliographic record
Abstract
Abstract Small diesel generators (< 10-kilowatt (kW) electricity output) are widely used as primary and backup sources of electricity in remote and other off-grid communities in Canada owing to their portability and durability. Major challenges associated with these generators include high costs of diesel and its transportation, as well as greenhouse gas (GHG) emissions. Biogas, which can be produced from the local waste or biomass, can be introduced into a diesel generator to potentially reduce diesel consumption and GHG emissions. The objective of this experimental study is to investigate the impact of engine speed, biogas composition, and intake temperature on the performance of a small biogasdiesel dual-fuel generator. This study was conducted using a 4-kW diesel generator with a normally aspirated, four-stroke, direct injection, single-cylinder diesel engine. A new intake manifold was installed on the engine to incorporate a biogas dosing port. The biogas was simulated by a mixture of compressed natural gas (composed of more than 95% methane), carbon dioxide (CO2), and nitrogen (N2). Exhaust gas temperature and composition were also recorded. Electrical load was maintained at 3.1 kW. The results of this study indicated that raising the engine speed from 1800 to 3600 rpm increased the diesel consumption rate, thereby increasing the overall GHG emissions. However, NOx emissions were reduced. The impact of the biogas composition was significant, especially the effect of CO2 when compared to N2. Increasing intake temperature improved engine efficiency particularly at higher biogas flow rates. The results highlighted the challenges associated with the application of biogas-diesel dual-fuel technology in small diesel generators with highspeed engines.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".