Experimental thermal and environmental impact performance evaluations of hydrogen-enriched fuels for power generation
Bibliographic record
Abstract
• A dual-fuel carburettor is used for fuel blend preparation. • Up to 20% (volume basis) of hydrogen is mixed with traditional fuels. • CO 2 emissions are reduced by 22–31% at a 20% hydrogen blend. • Hydrogen can offset the power output reductions with fuels other than gasoline. The transition to a low-carbon energy future requires a multi-faceted approach, including the enhancement of existing power generation technologies. This study provides a comprehensive experimental evaluation of hydrogen enrichment as a strategy to improve the performance and reduce the emissions of a power generator. A 3.65 kW power generation that is equipped with spark-ignition engine is systematically tested with five distinct base fuels: gasoline, propane, methane, ethanol, and methanol. Each fuel is volumetrically blended with pure hydrogen in ratios of 5 %, 10 %, 15 %, and 20 % using a custom-developed dual-fuel carburetor. The key parameters, including exhaust emissions (CO 2 , CO, HC, NO x ), cylinder exit temperature, electrical power output, and thermodynamic efficiencies (energy and exergy), are meticulously measured and analyzed. The results reveal that hydrogen enrichment is a powerful tool for decarbonization, consistently reducing carbon-based emissions across all fuels. At a 20 % hydrogen blend, CO 2 emissions are reduced by 22–31 %, CO emissions by 39–60 %, and HC emissions by 21–60 %. This environmental benefit, however, is accompanied by a critical trade-off: a severe increase in NO x emissions, which rose by 200–420 % due to significantly elevated combustion temperatures. Performance is notably enhanced; power output increased by 2–16 %, with hydrogen addition enabling lower-energy–density fuels like methane and propane to achieve performance parity with gasoline. Thermodynamic analysis confirms these gains, with energy efficiency showing marked improvement, particularly for methane, which increased from 42.0 % to 49.9 %. While hydrogen enrichment presents a viable pathway for enhancing engine performance and reducing the carbon emissions of power generators, the profound increase in NO x necessitates the integration of advanced control and after-treatment systems for its practical and environmentally responsible deployment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".