Modeling and optimization of dynamic isothermal compressibility features on flowability of Canarium schweinfurthii Engl nutshell powder
Bibliographic record
Abstract
The compressibility features (bulk density, tapped bulk density, porosity, coefficient of compressibility and Hauser ratio) of Canarium schweinfurthii engl. nutshell powder as it affects flowability during densification process were investigated. Three different moisture contents (10.13, 15.07 and 20.11% wet basis; w.b.) and particle sizes of 0.659 7, 1.26 and 2.05 mm were considered at pressure range of 2 to 10 MPa. The compressibility relationship with the factors were modelled and the optimum flow conditions were also determined. The obtained results showed that particle size and moisture content had incremental influence on the compression features studied except moisture content on bulk density. The compressibility of the nut shell powder increased from 17.44 to 28.18% and decreased from 29.41%to 18.79% as moisture content and particle size increased respectively. Medium particle size had the least Hausner ratio (1.16) and the best flow behaviour than other sizes for all the studied moisture contents. The linear model developed and its features had significant relationship with compressibility. The optimum values of pressure, moisture content and particle size required to achieve 17.45% compressibility for good flow are 4.88 MPa, 10.91% and 0.863 8 mm respectively.
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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".