Dynamic Performance of Continuous-Flow Mixing of Pseudoplastic Fluids Exhibiting Yield Stress in Stirred Reactors
Bibliographic record
Abstract
The core objectives of this work were to characterize and optimize the continuous-flow mixing of pseudoplastic fluids exhibiting yield stress in stirred reactors. To achieve these objectives, the effects of impeller type (for the seven axial-flow impellers A100, A200, A310, A315, A320, 3AH, and 3AM and the four radial-flow impellers R500, RSB, RT, and Scaba), impeller speed (50–800 rpm), impeller diameter ( T /3.2– T /1.6, where T is the tank diameter), impeller off-bottom clearance ( H /3.4– H /1.7, where H is the fluid height in the vessel), inlet and outlet locations (for the four configurations top inlet–top outlet, top inlet–bottom outlet, bottom inlet–bottom outlet, and bottom inlet–top outlet), pumping directions for axial-flow impellers (upward and downward pumping), fluid height in the vessel ( T /1.06– T /0.83), residence time (257–328 s), and jet velocity (0.317–3.24 m s –1 ) on the dynamic performance of the mixing vessel were explored. To identify nonideal flows, dynamic tests were conducted using the frequency-modulated random binary input of a brine solution with the feed. The mixing quality in the vessel was substantially improved by increasing the impeller diameter, increasing the residence time, optimizing the impeller off-bottom clearance, decreasing the fluid height, optimizing the jet velocity, and using the up-pumping axial-flow impeller. Applying these findings will lead to improved quality of products and more efficient use of power in continuous-flow mixing of yield-pseudoplastic fluids.
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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) | 0.001 | 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) | 0.000 | 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".