Optimization Study of Flocculation Mixing by Means of Grids
Bibliographic record
Abstract
The purpose of this study was to optimize flocculation mixing created by single grids. Double and triple biplane grids were vertically oscillated within a standard 2 L jar. The turbulent velocities were measured using a 2D laser doppler anemometer. The particle removal performance was observed based on the settled water turbidity. When a larger number of grids were applied, the same average volume velocity gradient (G) could be achieved at lower vertical grid speeds. A uniform and more gentle mixing was therefore created, producing high particle contacts and low particle breakup. The minimum settled water turbidity was also achieved at lower G values. A larger number of grids, especially of high solidity ratio, produced a wider range of G values with low settled water turbidity. These findings showed that the grid mixing performance could be optimized by applying a larger number of high solidity ratio grids. It was also found that grids, at their best arrangement, had a better performance than a standard flat blade impeller.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".