Phase Mixing Modeling in Multiphase Reactors Containing Gas Bubble: a Review.
Bibliographic record
Abstract
Multiphase reactors are often used as petrochemical, biological and pharmaceutical reactors. Understanding the mixing mechanism responsible for phase mixing is necessary for modeling purposes. Phase mixing may considerably affect reactor performance. In the literature, each phase mixing was often described by the Axial Dispersion Model (ADM), without physical justification. Some authors doubted the validity of the ADM and that it could represent the mixing of the phases and instead proposed other models. Also, some authors studied the fundamental mechanism responsible for phase mixing. For conversion and selectivity prediction purposes, the use of more complex phenomenological phase mixing models is essential when the kinetics are rapid and the interfacial mass transfer is limited. Many literature reviews concerning multiphase reactors have been published. These reviews, however, discuss phase mixing modeling based on the ADM and do not deal with the other phase mixing models. The aim of this work is to review the literature on phase mixing modeling, based on a mechanistic approach, for three kinds of multiphase reactors: the bubble column (solid free), slurry bubble column (small/light particles) and three phase fluidized bed (large/heavy particles) reactors. The common feature of these reactors is the presence of gas bubbles, which considerably influence the phase hydrodynamic. Hence, a mixing model proposed for one of these reactors may be conceptually representative of the mixing of the two others.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".