Axisymmetric acoustophoresis for paper pulp concentration
Bibliographic record
Abstract
In pulp and paper mills, mechanical processes such as screening and washing are commonly used to remove accumulated solid suspensions and concentrate the pulp. For environmental reasons and to optimize paper production, an emerging challenge is to develop alternative methods to concentrate paper pulp between 3 % and 6 % consistency for which the mixed pulp-water flow is complex. Among the proposed solutions in the literature, solutions based on acoustic levitation, also referred as acoustophoresis, of low-consistency pulp have been demonstrated as a potential solution for efficient pulp concentration and water recirculation. However, no sensitivity analysis on the ultrasound and physical parameters was proposed, limiting the extension to a realistic application. Thus, this paper presents a numerical modeling of acoustophoresis for pulp flow concentration in a pipe. For this purpose, the pulp flow is defined as a pseudo-homogenous fluid with a turbulent Low Re k- ∊ formalism, and the pulp particles are considered spherical and deflected by two acoustic forces, namely the acoustic radiation force and the Stokes drag force, both induced by an ultrasound wave generated along the walls of a circular pipe. The combined action of these two forces in the pulp flow enables to concentrate the particles at the center of the pipe. The influences of particle size and mechanical properties, fluid properties and ultrasound parameters are analyzed within a parametric study to optimize the particle deflection and the pulp concentration. The experimental feasibility of the industrial use of acoustophoresis for the concentration of paper pulp is demonstrated with a concentration gain up to 15 %.
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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 | 0.000 | 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) | 0.001 | 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".