Determination of Emission Factors for Co-firing Biomass and Coal in a Suspension Fired Research Furnace
Bibliographic record
Abstract
New regulations implemented by the Canadian federal government to limit greenhouse gas (GHG) emissions from coal burning power plants had sparked intense activity in the utility industry to find ways to reduce emissions. Several studies have indicated that carbon capture and storage (CCS) is not going to be economically available in the short term. Co-firing biomass appears to be an option for many of the coal-fired power plants, as Canada has a significant amount of biomass resources. Although biomass combustion can reduce greenhouse gas emissions, it can also generate other air pollutants. To determine emission factors for co-firing biomass and coal, pilot-scale tests were performed. These tests were conducted in CanmetENERGY’s 0.5 MW th pilot-scale pulverized fuel research furnace, which was configured with a dual-burner system, electrostatic precipitator, and baghouse. Gaseous emissions were recorded with two monitoring systems, and traditional methods for batch sampling of halogens, mercury, and particulate matter were implemented. Emission factors were developed for a 100% coal baseline, for two co-firing ratios of 20% and 55% biomass by heating value and biomass-only firing.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".