Empirical Modeling of Mean Residence Time in a Co‐Rotating Twin‐Screw Extruder with Rice Flour
Bibliographic record
Abstract
ABSTRACT Mean residence time of rice flour in a twin‐screw extruder was determined using a blue tracer. Variables studied included moisture content, screw speed, barrel temperature, and screw configuration. Mean residence time increased with the increase of the barrel temperature and with the addition of reverse and kneading elements. Mean residence time was significantly related to screw speed, moisture content, die pressure, and screw configuration ( P < 0.05). An empirical model was developed to predict mean residence time with the ability to reflect the changes of the barrel temperature and screw configuration. The effects of different extrusion operating conditions including screw speed, moisture content, barrel temperature, and screw geometry on the mean residence time were considered in the model. The validity of the developed model was extensively evaluated and verified using different screw geometries and other processing variables. The mean residence times predicted by the developed model are in good agreement with the experimental data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".