Performance of Four Axial Flow Impellers for Agitation of Pulp Suspensions in a Laboratory-Scale Cylindrical Stock Chest
Bibliographic record
Abstract
Axial flow impellers are commonly used for pulp suspension agitation. Pulp fiber suspensions are non-Newtonian and exhibit a yield stress. In mixing operations, a ‘cavern’ (region of active motion) is created around the impeller, with the size of the cavern affecting the quality of mixing attained. In this work, the cavern size produced by four different axial flow impellers in a C m = 3% (mass concentration) hardwood pulp suspension was measured using electrical resistance tomography (ERT) and by analysis of dynamic mixing tests. Cavern size is shown to depend on impeller performance as characterized by power number, N P, and axial force number, N f . At an equal power consumption of 0.53 kW/m 3 the largest cavern was produced by the impeller having the largest values of N P and N f . The measured cavern volumes compared well with predictions of the axial force model developed by Hui et al. [Hui, L. K.; Bennington, C. P. J.; Dumont, G. A. Cavern formation in pulp suspensions using side-entering axial-flow impellers. Chem. Eng. Sci. 2009, 64, 509], which accounted for interaction between the cavern and the vessel walls. When the cavern just filled the vessel volume, the time constants determined using the dynamic mixing test data reached 90% of their theoretical values (with the estimated standard deviation of ±10%), indicating that the chest approached an ideal dynamic response (complete mixing) with the onset of complete motion in the chest.
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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 | 0.001 | 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".