Discrete Element Modeling of Hemp Particle Separation Using a 3D Vibratory Separator.
Bibliographic record
Abstract
Discrete Element Modeling of Hemp Particle Separation Using a 3D Vibratory Separator.Canadian Biosystems Engineering/Le génie des biosystèmes au Canada 55: 2.23-2.31.Existing cleaning equipment was ineffective for cleaning processed hemp material (a mixture of fibre, core, and chaff).One of the major obstacles was the lack of the knowledge on the cleaning process.In this study, a 3D vibratory separator (screen-type) was experimentally studied for cleaning processed hemp material through separating chaffs (small particles) from the other particles (larger particles) in a mixture.In the experiment, the separation efficiency of the 3D vibratory separator was measured for different horizontal and vertical vibratory settings of the separator.Results showed that the separation efficiency varied from 23 to 86%, depending on the vibratory settings.The low separation efficiency was due to the fact that chaff was held within the entangled fibre, which prevented chaff from passing through the screen.A numerical model was developed to simulate the 3D vibratory separator using the discrete element method (DEM).The main model components included virtual particles of processed hemp material as well as a virtual 3D vibratory separator.The model is able to deal with mixtures of different particle sizes and different portions of fibre, core, and chaff particles, and predict the separation rates and efficiencies of the 3D vibratory separator.When compared with the measured separation rate, the simulated separation rate was higher towards the end of separation processes due to inexistence of fibre tangle problem in the virtual hemp particles.However, the trend of simulated values could adequately explain that of the measurements.Keywords: Hemp,
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".