Effect of hydrogen‐enriched biogas induction on combustion, performance, and emission characteristics of dual‐fuel compression ignition engine
Bibliographic record
Abstract
Abstract Gaseous fuel induction in a compression ignition (CI) engine has picked up much attention over the most recent few years, particularly when it is produced by using renewable sources. In this study, hydrogen‐enriched biogas (HEB) was used as a gaseous fuel and co‐combusted in dual‐fuel CI engine. Experimentation was carried out on a 3.5‐kW CI engine test rig in the brake mean effective pressure range between 0 and 3.5 bar, as well as, HEB between 0.1 and 0.5 kg/hr. HEB induction effects on various engine characteristics (combustion, performance, and emission) were studied at rated engine speed (1,500 rpm). Results revealed that the peak cylinder pressure and ignition delay period increased with increasing HEB proportion (0.1 to 0.5 kg/hr) as compared with diesel mode. The calorific value of HEB (57.0 MJ/kg) is higher than diesel (42.0 MJ/kg), which led to improved BTE in dual‐fuel mode. The emission results showed that with an increase of HEB rates, the NOx emission mildly decreases, but smoke opacity and hydrocarbon emissions majorly reduce. Thus, the HEB induction has great potential to be a feasible technological solution to overcome low BTE and high hydrocarbon emissions in biogas operated CI 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 | 0.000 |
| 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) | 0.001 | 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".