Emissions and Properties of Bio-oil and Natural Gas Co-combustion in a Pilot Stabilised Swirl Burner
Bibliographic record
Abstract
Fast pyrolysis oil, or bio-oil, has been investigated to replace traditional fossil fuels in industrial burners. However, flame stability is a challenge due to its high water content. In order to address its instability, bio-oil was co-fired with natural gas in a lab scale 10kW swirl burner at energy ratios from 0% bio-oil to 80% bio-oil. To evaluate the combustion, flame shape, exhaust and particulate emissions, temperatures, as well as infrared emission were monitored. As the bio-oil energy fraction increased, NO emissions increased due to the nitrogen content of bio-oil. CO and particulate emissions increased likely due to carbonaceous residue exiting the combustion zone. Unburnt Hydrocarbon (UHC) emissions increased rapidly as combustion became poor at 60-80% bio-oil energy. The temperature and infrared output decreased with more bio-oil energy. The natural gas proved to be effective at anchoring the bio-oil flame to the nozzle, decreasing instances of extinction or blowout.
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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".