CFD‐PBE‐PBE simulation of an airlift loop crystallizer
Bibliographic record
Abstract
Abstract A complete solver (CFD‐PBE‐PBE) for crystallization processes in an airlift loop crystallizer is developed in OpenFOAM (open‐source field operation and manipulation) in this work. It combines computational fluid dynamics (CFD) with population balance equations (PBE) for both gas bubbles and crystals. Models for gas‐liquid mass transfer and chemical reaction are included as well in the solver. PBE describing bubble coalescence and breakage is solved by the cell average method. Primary nucleation, secondary nucleation, and particle growth are considered in the PBE describing the crystallization process. The solver is validated with the formation of calcium carbonate via the reaction of CO2 with Ca(OH)2 solution in an airlift reactor. Effects of the chemical enhancement factor and crystallization kinetics on predictions are systematically investigated. Variation of predicted pH value, concentration of Ca2+, mean particle size, and crystal size distribution (CSD) with time is in qualitative and semi‐quantitative agreement with the published experimental data, when the appropriate crystallization kinetics are used. Effects of operation parameters such as initial concentration and superficial gas velocity are further numerically examined. The increase in superficial gas velocity results in the increasing consumption rates of OH‐ and Ca2+, while the particle diameter seems unchanged in the present simulation. A higher initial concentration of reactants will lead to a smaller particle diameter and a narrower CSD. The predicted results indicate that the developed solver is feasible, and can be used for the design and scale‐up of airlift loop crystallizers.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".