Mixing Process of Binary Polymer Particles in Different Type of Mixers
Bibliographic record
Abstract
The main objective of this paper is to investigate the mixing process of free flowing polymers binary mixtures at different densities and colors in two different mixers, namely the bubbling fluidized bed and V-mixer. The mixing of solids was studied by analyzing the variation of the proportions of the marked particles with time and position in the bed or in the mixer. The variation of mixture composition based on the samples was incorporated into Lacey mixing index which describes the degree of mixing of the particles at particular time. The performance and the mixing behavior in these two mixers were also presented. Results showed that gas velocity and bed depth were important parameters influencing solids mixing in a bubbling fluidized bed. While the rotation speeds and filled up levels were proved as important parameters in influencing the solids mixing in V-mixer. From the results, complete mixings were attained at a bed depth of 17 cm and gas velocity of 1.38Umf in the fluidized bed and 40 % filled up level and 40 rpm in the V-mixer. From the energy consumption point of view, it was found that the fluidized bed mixer offers the most efficient and economical process compared to V-mixer.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".