Assessment of the Mixing of Polydisperse Solid Particles in the Rotary Drum and Slant Cone Mixers Using Discrete Element Method
Bibliographic record
Abstract
In spite of wide applications of powders in industry, there is a lack of sufficient knowledge regarding the mixing of poly-disperse particles in rotary drum and slant cone mixers. The main objective of this study was to explore the mixing quality of mono-disperse, bi-disperse, tri-disperse, and poly-disperse particles inside rotary drum and slant cone mixers as a function of the drum speed, particle size, agitator speed, and the initial loading method through the discrete element method (DEM). To achieve this objective, experimental work and simulations were carried out. DEM results were validated using experimental data obtained from both sampling and image analysis techniques. DEM simulation results were in good agreement with the experimentally determined data, both qualitatively and quantitatively. Three major loading methods were defined: side-side, top-bottom, and back-front. Also, the mixing metric was utilized to measure the mixing quality. For bi-disperse particles inside the slant cone mixer, the mixing index increased to a maximum and decreased slightly before reaching a plateau at the drum speed of 15 rpm with different loading methods as a direct result of the segregation of particles of different sizes. The same behavior was observed in the rotary drum for bi-disperse, tri-disperse, and poly-disperse particles. The effect of agitator speed on the mixing performance for bi-disperse particles inside the slant cone mixer was also investigated. The addition of the agitator increased the mixing quality and reduced the segregation of particles with different sizes. The best mixing qualities for the tri-disperse and poly-disperse particles inside the rotary drum were recorded for the top-bottom smaller-to-larger loading method. For the slant cone mixer, highest mixing indices for tri-disperse and poly-disperse particles with the top-bottom smaller-to-larger loading method were obtained at drum speeds of 15 and 55 rpm, respectively. The impact of segregation for both mixers was reduced by introducing additional intermediate size particles.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".