An Effect of Blending Ratio on Mechanical and Thermal Properties of the Sawdust-Cocoa Pod Briquettes
Bibliographic record
Abstract
The present work aims to utilize wastes of sawdust and cocoa pod as the raw materials of low pressure densified briquettes and to investigate an effect of blending ratio on mechanical and thermal properties of the briquettes. The weight ratio between sawdust and cocoa pod are 3:1 (SD75CP25), 1:1 (SD50CP50), and 1:3 (SD25CP75). The compaction process is performed at room temperature and under low compacting pressure of 0.1 MPa. The quality of the briquettes is evaluated in terms of their mechanical properties (relaxed density, stable density, water resistance index, and compressive strength), and their thermal properties (higher heating values). The results indicate that waste of sawdust and cocoa pod have the potential as the raw material of low pressure densified sawdust-cocoa pod briquettes. Based on the stable density and the higher heating value, the SD50CP50 briquette has the best properties in the present work. The stable density and the higher heating value of the SD50CP50 briquette are 452.74 kg/m3 and 16.34 MJ/kg, respectively. The values are comparable to the values of other densified briquettes obtained by other researchers.
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 | 0.000 | 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".