Study on Density, Hardness, and Moisture Uptake of Torrefied Wood Pellets
Bibliographic record
Abstract
Torrefied pellets, a transportable renewable energy source, have a higher energy density than the regular wood pellets (control pellets). The quality of torrefied pellets is determined mainly by the density, hardness, and the hygroscopicity or moisture uptake. In this study, the density and the hardness of torrefied pellets were systematically examined by using torrefied samples prepared at different conditions in a press machine. The hygroscopicity of prepared torrefied pellets was evaluated in a humidity chamber by measuring the moisture uptake rate of control and torrefied pellets. The results showed that the density and the hardness of torrefied pellets mainly depended on the densification die temperature and the weight loss of torrefied samples. To make strong torrefied pellets of high density and low moisture uptake from 30 wt % weight loss torrefied samples, a die temperature of 230 °C or above was needed. Preconditioning torrefied samples to a moisture content of ∼10% can improve the quality of torrefied pellets. The moisture uptake of torrefied pellets was more sensitive to the weight loss of torrefaction and the relative humidity of the storage environment. The saturated moisture uptake of torrefied pellets made from 30 wt % weight loss torrefied samples was at least 40% lower than the control pellets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".