Cold‐model investigation of the effect of dispersed phase inlet on the dispersion uniformity in a liquid‐liquid cyclone reactor for ionic liquid‐catalyzed isobutene alkylation
Bibliographic record
Abstract
Abstract A liquid‐liquid cyclone reactor was proposed for ionic liquid‐catalyzed isobutane alkylation to improve the mixing efficiency and accelerate the separation between alkylate and ionic liquid. The Eulerian‐Eulerian multiphase flow model and Reynolds stress model (RSM) were used to simulate the flow field in the reactor. A parameter, named the dispersion uniformity of dispersed phase, β, was used to quantify the dispersibility of the dispersed phase in the reaction chamber of the reactor. Moreover, the effects of the structural parameters of the dispersed phase inlet (slot) on the dispersion uniformity of the dispersed phase were investigated. As can be seen from the results, the angle of the slot has an effect on the inlet velocity direction, while the other structural parameters have an effect on the inlet velocity magnitude. The shear force is the impetus for the dispersion of the dispersed phase into the continuous phase, and the relative velocity is the cause of the shear force. Therefore, by analyzing the effect of the structural parameters of the slot on the dispersion uniformity, it can be deduced that the dispersion of the dispersed phase performs well under a suitable inlet velocity ratio range of the continuous phase to the dispersed phase (2.5‐3.5).
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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".