Factors Affecting Fines Flocculation Performance with Cofactor-Polyethylene Oxide
Bibliographic record
Abstract
In literature, neutral polyethylene oxide (PEO) flocculated fines at low shear rates, while with cofactor (CF) addition, the formed CF-PEO complex showed larger ability to bridge fines, producing flocs. In this work, some process factors were found having significant effects on fines flocculation. Increases in CF to PEO ratio at constant PEO enhanced the bridging bonds, causing increases in flocculation initial rate (efficiency), amplitude (floc size), and fastness (a decrease in characteristic time). On the other hand, an increase in stirring rate (shear rate) in flocculation vessel caused decreases in initial rate and amplitude, and an increase in the fastness. All runs showed transient flocculation; the amplitude increased with time, reached maximum at equilibrium, and then started to decrease showing deflocculation. In brief, the CF to PEO ratio and the shear rate were found important parameters in mill operation, having significant effects on flocculation efficiency, fastness, and floc size.
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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 | 0.002 |
| 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) | 0.001 | 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".