Overall and Fluid-to-particle Heat Transfer Coefficients associated with Canned Particulate non-Newtonian Fluids during Free Bi-axial Rotary Thermal Processing
Bibliographic record
Abstract
Abstract Heat transfer to canned particulates in non-Newtonian fluids (Nylon particles suspended in aqueous carboxymethyl cellulose - CMC - solution) during fixed and free bi-axial rotation was studied in a pilot-scale, full water-immersion single-cage rotary retort. A response surface methodology was used in order to study the effect of different process parameters, including rotational speed (7-23 rpm), CMC concentration (0.0-1.0%) and retort temperature (110-130C), at five levels of each, on overall heat transfer coefficient (U) and fluid to particle heat transfer coefficient (hfp) in both rotation modes. The analysis of variance showed that the rotational speed, CMC concentration and retort temperature were significant (p < 0.05) factors for hfp in the following order: rotation speed > CMC concentration > retort temperature; however, only rotational speed and CMC concentration were significant (p < 0.05) factors for U. With an increase in rotational speed and retort temperature, there was an increase in the associated U and hfp values; however, increasing the CMC concentration resulted in the opposite. Using the numerical optimization of the Design Expert software, optimum heat transfer was found at a rotational speed of 20 rpm, CMC concentration of 0.6% and retort temperature of 126C. T-test revealed that both U and hfp were significantly (p < 0.05) higher in the free bi-axial mode as compared to the fixed axial mode of rotation.
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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 | 0.000 | 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".