Critical experimental evaluation of hydrogen blends with conventional fuels for enhanced power generator performance
Bibliographic record
Abstract
• Gasoline, methane and propane are blended with hydrogen up to 20%vol. • A dual-fuel carburettor is included in the engine of a commercial power generator. • Methane/hydrogen blends show better performance in terms of emissions. • A 20% hydrogen compensates the power reduction with methane. This experimental work explores the use of hydrogen blends with gasoline, methane, and propane in power generators. A commercial gasoline engine-driven stationary power generator with a capacity of 3.65 kW is utilized during the tests. A dual-fuel carburetor is included in the engine to adjust the desired fuel mixture after a set of modifications. Gasoline, methane, and propane are then blended volumetrically with hydrogen with the ratios of 5 %, 10 %, 15 %, and 20 %. The tests with 100 % gasoline, methane, and propane are also conducted to make a proper comparison. The power output of the generator, temperature at the cylinder exit, and exhaust gases are experimentally tested, and the obtained measurements are then analyzed. The results reveal that increasing hydrogen content up to 20 % significantly has reduced CO 2 emissions by 42.8 % for gasoline, 46.9 % for propane, and 37.7 % for methane, while also reducing CO emissions by 44.8 %, 50 %, and 40 % for gasoline, propane, and methane, respectively. However, NO x emissions tend to increase significantly with hydrogen addition, particularly for propane (186.3 % increase) and methane (154.8 % increase). The power output of the generator and efficiency generally improved, with propane showing the highest increase in power (10 %) and methane exhibiting the most significant gain in efficiency (reaching parity with gasoline at 20 % hydrogen). With increasing hydrogen content, the operating cost per hour has decreased for all fuel types.
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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".