Bibliographic record
Abstract
Biomass co-firing within existing infrastructure of pulverized coal utility boilers is viewed as a practical near-term means of encouraging renewable energy while minimizing capital requirements, maintaining the high efficiency of pulverized coal boilers and reducing the emissions. Numerical investigation of coal/biomass co-firing is presented in this study. Co-combustion of biomass and coal is a complex problem that involves gas and particle phases, along with the effect of the turbulence on the chemical reactions. The transport equations for the continuous phase (gas) and discrete phase (spherical particles) are solved respectively in the Eulerian and Lagrangian frame of reference. The mathematical models used for co-pulverized coal/biomass particles combustion consist of models for turbulent flow (RNG k-ε model); gas phase combustion (two mixture fractions/PDF model: one mixture fraction is used for the fuel (char) and the second for the volatiles); particles dispersion by turbulent flow (stochastic tracking model); coal/biomass particles devolatilization (two competing rates Kobayashi model); heterogeneous char reaction (kinetics/diffusion limited rate model); and radiation (P-1 radiation model). The coal used is a Canadian high sulfur bituminous coal. The coal was blended with 5 to 20% wheat straw (thermal basis) for co-firing. The effect of the percentage of biomass blended with coal on the velocity field, temperature distribution, particles trajectories and pollutant emissions at the exit of the furnace is presented in this paper. One important result is the reduction of NO and CO2 emissions when using co-combustion. This reduction depends on the proportion of biomass (wheat straw) blended with coal.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".