Bibliographic record
Abstract
Abstract A mathematical model was built and used to show the motion of particles in a cascade rotary drum dryer. In a cascade rotary drum the flights pick up the particles at a number of points in the lower half of the drum while, in the upper half, the particles fall freely. A model is derived where the drag force exerted on the particles throughout the falling period is emphasised. The motion of the particles in a rotary drum is described by three actions: Cascade; Kiln and Bouncing. In this study a horizontal rotary drum was used where both the kiln and bouncing actions have minimal effect, therefore, the focus is on the cascade motion of the particles. A characteristic of the model is the “falling number” which is found to be dependent on the curtain properties. The model has demonstrated its ability to predict the effect of many important parameters such as drying medium velocity, drum rotation speed, particle size and feeding flow rate. It has been shown that increasing the drying medium velocity by 2.5 times results in an 85% decrease in the residence time. Also, the number of falling is shown to be limited and a function of the drum rotation speed, in this case 0.59 falling per second. An important feature of this model is the ability to predict the mean resident time, contact time interval and the resting time interval. The maximum error between the predicted and the measured data was <10%.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".