Effect of Pyrolysis Temperature and Wood Species on the Properties of Biochar Pellets
Bibliographic record
Abstract
Thermal treatments such as torrefaction and fast pyrolysis are commonly employed methods to produce biofuels with high-energetic properties. In this study, wood chips were heat-treated at different temperatures of torrefaction (315 °C) and fast pyrolysis (400 and 454 °C) to form energetic pellets. Three softwoods, jack pine (JP), balsam fir (BF), and black spruce (BS), were evaluated. Pellets are produced using 20% moisture content and 15% pyrolytic lignin as a binder. Untreated- and treated-wood residues were characterized by surface chemistry, elemental analysis, and chemical composition, whereas all pellets were characterized in terms of density, high heat value (HHV), and durability. Results showed that both thermal treatments caused significant changes in the physicochemical structure of wood residues. Using temperatures higher than 315 °C leads to the disappearance of hydroxyl groups, a decrease in oxygen and hydrogen contents, and an increase in carbon content. Regardless of the treatment temperature, pellets made from heat-treated JP had the best durability (93%). In contrast, the calorific values of wood-treated pellets reached up to 31 MJ/kg, compared to untreated-wood pellets (19 MJ/kg). Thus, the densification of the thermal-treated wood residues represents a potential approach for producing biofuels with high energetic value.
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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) | 0.001 | 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".