Impact of process conditions on the density and durability of wheat, oat, canola, and barley straw briquettes
Bibliographic record
Abstract
The present study is to understand the impact of process conditions on the quality attributes of wheat oat, barley, and canola straw briquettes. Analysis of variance indicated that briquette moisture content and initial density immediately after compaction and final density after 2 weeks of storage are strong functions of feedstock moisture content and compression pressure, whereas durability rating is influenced by die temperature and feedstock moisture content. Briquettes produced at a low feedstock moisture content of 9 % (w.b.) yielded maximum densities >700 kg/m3 for wheat, oat, canola, and barley straws. Lower feedstock moisture content of <10 % (w.b.) and higher die temperatures >110 °C and compression pressure >10 MPa minimized the briquette moisture content and maximized densities and durability rating based on surface plots observations. Optimal process conditions indicated that a low feedstock moisture content of about 9 % (w.b.), high die temperature of 120–130 °C, medium-to-large hammer mill screen sizes of about 24 to 31.75 mm, and low to high compression pressures of 7.5 to 12.5 MPa minimized briquette moisture content to <8 % (w.b.) and maximized density to >700 kg/m3. Durability rating >90 % is achievable at higher die temperatures of >123 °C, lower to medium feedstock moisture contents of 9 to 12 % (w.b.), low to high compression pressures of 7.5 to 12.5 MPa, and large hammer mill screen size of 31.75 mm, except for canola where a lower compression pressure of 7.5 to 8.5 MPa and a smaller hammer mill screen size of 19 mm for oat maximized the durability rating values.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".