Development of a CFD–PBE coupled model for the simulation of the drops behaviour in a pulsed column
Bibliographic record
Abstract
The pulsed column is a widely used technology for liquid–liquid extraction processes in various industries. In this work, the use of this technology has been extended to perform continuous precipitation. An original process of continuous precipitation in emulsion in a pulsed column is thereby developed. A thorough understanding of the behaviour of the dispersed phase inside the column helped to achieve process optimisation and is the purpose of this paper. In this aim, a coupled computational fluid dynamics (CFD)–population balance equation (PBE) approach was developed for the simulation of this original process, and allows the determination of the mean droplet size, which is a key parameter. On one hand, breakup and coalescence kernels for the PBE were selected by performing homogenous type experiments in a stirred tank reactor. The parameters of those kernels were adjusted by fitting the models' parameters to the measured droplets size distribution (DSD) in the stirred tank. One another hand, the continuous phase flow inside the pulsed column was investigated by CFD and has been validated using particle image velocimetry (PIV) data. The latter helped us to choose the best turbulence model representing the flow inside the pulsed column. Finally, the coupled CFD–PBE model was implemented using the quadrature method of moments (QMOM) in the CFD code ANSYS‐Fluent® to determine the mean droplet size inside the column.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".