Pilot-Scale Examination of Mixing Liquid into Pulp Fiber Suspensions in the Presence of an In-Line Mechanical Mixer
Bibliographic record
Abstract
The quality of liquid mixing into the main stream for an in-line mechanical mixer was investigated for water and pulp suspensions over a range of mass concentrations (0–3.0%), main-stream velocities (0.5–3.0 m/s), jet velocities (3.8–12.6 m/s), and rotational speeds (0–800 rpm) based on electrical resistance tomography and a modified mixing index, derived from the coefficient of variation of conductivity values. The mixing quality was worse when the jet penetrated to the far wall of the pipe for all fiber mass concentrations investigated, whereas this only applied at higher mass concentrations without the impeller. For water flow, the residence time had a significant effect on mixing at higher impeller speeds. With the impeller present, the mixing quality in pulp suspensions improved substantially and was similar to that for water when the flow approached the turbulent regime, with a considerably lower main-stream velocity required for mixing compared to a tee mixer alone. At higher mass concentrations, the energy supplied was insufficient to provide the same level of turbulence as that in water, even at the highest main-stream velocity and impeller speed examined. Improved mixing with increasing impeller speed primarily occurred in the high-shear zone around the impeller, with turbulence decaying rapidly downstream, likely aided by reflocculation.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".