2-D modeling of torrefaction of a large biomass particle: effect of <i>L/D</i> ratio, internal convection and shrinkage
Bibliographic record
Abstract
Torrefaction of a large biomass particle was investigated using a transient 2-D model, including primary and secondary reactions kinetics and heat transfer, internal convection, and shrinkage. The model predictions matched with the experimental results within +2%. Spatial and temporal distributions of temperature, residual mass fraction, velocity, and pressure inside the particle and the effect of reactor temperature, residence time, particle size, and shrinkage on the torrefaction behaviour were investigated. Evolution of moisture, volatiles and gases due to drying and torrefaction increase the pressure inside the particle, setting up a velocity field and internal convection. Average velocity and pressure reached a peak during the initial drying period due to moisture evolution, and a subsequent second peak due to the formation of volatiles and gases by torrefaction at a reactor temperature > 493 K. Internal convection influenced torrefaction significantly but the particle shrinkage did not impact it appreciably. The degree of overshoot of the particle center temperature above the reactor temperature increased with the reactor temperature and the particle diameter. Simulations suggest that a reactor temperature of 550 K and residence of about 30 min would be suitable for torrefaction of a particle with L = D = 25.4 mm.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".