A classification AI model to predict choking of vibrating screen based on DEM and machine learning
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
Screening is a complicated process for classifying granular materials according to size. Choking is a vital issue in screening. It may occur when the particle flow along a screen is too slow, but slow particle flow and long residence time are beneficial to sieving performance. Therefore, a model to judge whether choking happens is useful for finding optimal operating conditions. Here, a classification model to predict screen choking is proposed by combining DEM simulation and machine learning. The model can consider various key controlling variables for particle properties and operating conditions. Using the model, safe operation condition regions without choking can be identified. Then, combining the model with our previous machine learning based process model, we can design a screening process with the desired performance. The work also shows a way of using machine learning to predict critical phenomena in particle flow. • Classification AI model for screen chocking developed based on DEM data. • Model can be used to obtain safe operating conditions for screening. • Process optimization by combining classification and predictive AI models • Model predicts feeding threshold for screen chocking. • Classification AI model for critical phenomena of granular matter
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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