Two-dimensional modeling of torrefaction of a large biomass particle
Bibliographic record
Abstract
A two-dimensional (2-D) model is developed to predict the torrefaction behavior of a large wet biomass particle. Although one-dimensional (1-D) model is found to be adequate for L/D ≥ 6, the necessity of using 2-D model at lower L/D ratios and higher torrefaction temperature is established. Errors up to 18% are observed in predicted mass fractions between 1-D and 2-D models. The center temperatures differed more, up to 96%, between z = 0 and z = L/2 in 2-D model which is not captured by the 1-D model. The model predictions agree well with the experimental results of the present authors and others. The evolution of the temperature profile is found to govern the mass fraction profile. At higher reactor temperature, three distinct zones are visible in the contour plots: peripheral fully torrefied zone, intermediate torrefying zone, and core with unreacted virgin biomass zone. Simulation studies show the formation of two symmetric annular hot spots at the ends, which move inward axially and subsequently merge at the center, the rate being faster for smaller L/D ratio. However, 1-D model does not provide such insight. The effects of reactor temperature, particle size, the residence time, and the initial moisture content on the torrefaction behavior are investigated.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".