Characteristics of Wood Pellets from Sengon Tree (Albizia Chinensis) Waste Materials for Eco-Friendly Fuel
Bibliographic record
Abstract
The Sengon tree (Albizia Chinensis) presents a promising avenue for biomass utilization due to its inherent natural adhesives, namely lignin and hemicellulose, streamlining the pelletization process without additional adhesive materials.This paper provides a comprehensive analysis of Sengon wood pellets' characteristics.To ensure high-quality pellets, minimizing inorganic elements such as alkali metals (K, Na), and reducing Cl, S, and Si content is essential to mitigate impurities, scale formation, and combustion boiler corrosion.Initial findings indicate that Sengon wood pellets exhibit a carbon content increase from 13.41% to 15.02% when dry, a slight sulfur content increase from 0.08% to 0.09%, and a calorific value of 4302 Kcal/kg.With a wood pellet density of 796 kg/m³ and a burning rate of 321.15 g/second, SEM-EDX testing confirms their suitability as fuel.These results underscore the potential of Sengon wood as a viable and sustainable biomass resource for thermal energy production, with implications for improving pellet quality and combustion efficiency.
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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 | 0.001 | 0.001 |
| 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".