<scp>CFD</scp> investigation of pelletization effect on co‐firing coal with wheat straw
Bibliographic record
Abstract
Abstract Biomass co‐firing in existing coal‐fired power plants has been proven successful and practised in many installations worldwide as a greener alternative to coal. Attempts to overcome the low efficiency of transporting raw biomass have resulted in global wood pellets market growth in the past decade. This research studies the effect of biomass densification on biomass co‐firing with coal. A three‐dimensional CFD model was used to simulate the co‐firing of coal and wheat straw. The model was verified by comparing the simulation results and experimental data presented in the literature. The experimental and predicted results were found to be in good agreement and exhibit the same general trends in terms of the gas species (O 2 , CO, CO 2 , and H 2 O) concentrations. The effect of pelletization on biomass particle combustion was then investigated by considering particle shrinkage due to compression during pelletization. The baseline case is for uncompressed particles having particle density of 600 kg/m 3 , whereas the two cases of densified particles have particle densities of 800 kg/m 3 and 1000 kg/m 3 . Results show that, when compared to the baseline case, compressed particles experience slower volatilization and surface reaction rates, and hence a greater percentage of unburnt carbon. Larger wheat straw particles (>894 μm) have different aerodynamic interaction compared to smaller particles in terms of residence time and particle trajectory. CFD results also show that compressed particles would exhibit lower NO emission, and NH 3 as the intermediate product from biomass volatile‐N has contributed to the reduction in NO emission during the volatilization process.
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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) | 0.002 | 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".