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.
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".