Combustion and air emissions from co‐firing a wood biomass, a Canadian peat and a Canadian lignite coal in a bubbling fluidised bed combustor
Bibliographic record
Abstract
Abstract The effects of particle size, fuel blending ratio, moisture content and excess air ratio on combustion efficiency and air emissions (CO 2 , CO, SO 2 and NO x ) from the co‐combustion of white pine or peat with a Canadian lignite coal, were examined in a pilot‐scale bubbling fluidised bed combustor. Pelletising was important for the efficient combustion of wood due to its high volatile content. Co‐firing lignite and pine pellets gave a proportional reduction in SO 2 and NO x emissions with blending ratio, while co‐firing of peat and lignite resulted in increased SO 2 emissions, but decreased NO x emissions. Moisture promotes combustion but with increased CO emissions, and results in increased NO x emissions, and decreased SO 2 emissions. High excess air decreased CO, but moderately increased SO 2 and NO x emissions. © 2011 Canadian Society for Chemical Engineering
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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 | 0.001 | 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 | 0.001 |
| 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".