Analysis of cohesive mannitol particle mixing: A comparative study of machine learning methods
Bibliographic record
Abstract
An in-depth analysis of the cohesive powder blending of Mannitol with different sizes within a double-paddle mixer was conducted, emphasizing the development of a robust quantitative simulation model via the Discrete Element Method (DEM). Leveraging datasets from DEM, the Random Forest (RF), Artificial Neural Network (ANN), and Multivariate Polynomial Regression (MPR) algorithms were employed to construct predictive models for the system's mixing index. Our key findings indicate that impeller speed and mixing time are the most critical factors significantly influencing mixing performance. Through a comprehensive comparison, RF demonstrated the highest accuracy in predicting the mixing index among the ML techniques tested. The study examined essential operational parameters—such as fill level, impeller speed, and particle characteristics—to assess their significant impacts on blending efficiency and product quality. This study contributes to the field by providing a calibrated DEM model and showcasing the effective integration of DEM with machine learning to predict and optimize mixing performance, thereby reducing computational efforts and enhancing industrial mixing processes for cohesive particulate systems.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".