Residence Time Distribution of Particles in a Screw Feeder: Experimental and Modelling Study
Bibliographic record
Abstract
Experiments were conducted to investigate the effects of screw speed and screw feeder inclination on the residence time distribution (RTD) of particles in a screw feeder via a pulse stimulus response technique. Two models based on Markov chains were developed to simulate particle flows within and between pitches. In upward and horizontal screw feeder inclination cases, a three‐parameter two‐dimensional Markov chain model consisting of parallel active and stagnant zones fitted well with the experimental RTD data, with correlation coefficients (R2) higher than 0.98, and gave a clear physical meaning for the parameters introduced. In these cases, a high screw speed or a horizontal inclination induced a high probability of forward movement from a pitch to the next pitch (f) during each rotation period of the screw, and a low ratio of stagnant zone to active zone (r) in a pitch. The upward screw feeder inclination yielded a higher diffusion probability from stagnant zone to active zone (d). In the downward screw feeder inclination case, a one‐dimensional Markov chain model without a stagnant zone was in agreement with the corresponding experimental data. The analysis showed that during each rotation period of the screw, the particles in a pitch could be transferred not only to the next pitch but also to the following two pitches.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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".