Empirical Modeling of Die Pressure, Shaft Torque, SME, and Product Temperature of Rice Flour in a Corotating Twin‐Screw Extruder
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT Empirical models for predicting die pressure, product temperature, shaft torque, and specific mechanical energy (SME) input based on rice flour extrusion using a DNDL‐44/28D Buhler twin‐screw extruder are presented. The models incorporate the effects of shear rate, barrel temperature, moisture content, flow rate, and screw geometry. The models were tested using rice flour at various screw configurations and extrusion conditions. Die pressure is a function of moisture content, product temperature, and flow rate. By testing the die pressure model, we found that, within the experimental range tested, die pressure was not significantly affected by barrel temperatures and screw configurations. Product temperature and shaft torque are functions of shear rate, moisture content, flow rate, barrel temperature, and screw configuration. Introducing the effect of screw configuration into the models for temperature and shaft torque resulted in an overall improved model performance. Predictions of various models gave good results. Validations of various models were verified using different screw geometries and other processing variables with reasonable accuracy. Extrusion tests indicated that the developed predictive models can be of use for extrusion processing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it