Development of a hybrid pressure drop and liquid holdup phenomenological model for trickle bed reactors based on two‐phase volume averaged equations
Bibliographic record
Abstract
Abstract A model with a high predictive quality to estimate pressure drops and liquid holdups in trickle bed reactors (TBR) is yet necessary to assist in design, up scaling, and the implementation of new processes tasks. The currently available models to estimate pressure drops and liquid holdups on TBRs exhibit important deviations, which lead to uncertainties in their applicability. To overcome the limitations in prediction deviations in the currently available models, a new model is developed based on the volume averaged two‐phase transport equations in a porous media, as developed by Whitaker. In order to develop a model that could simultaneously predict pressure drops and liquid holdup with a high accuracy, the developed model was coupled with a modification of the extended slit model reported in the literature, leading to a new hybrid model with enhanced predictability. Experimentally determined pressure drops and liquid holdup in a column with a 0.14 m internal diameter and a height of 2 m, packed with different extrudate geometries, cylinders, trilobes, and quadlobes, were used to determine the model parameters and to verify the quality of the proposed hybrid model predictions. The developed model, when compared with the experimentally determined data of pressure drops showed mean squared errors (MSE) of 0.89%, 2.31%, and 1.22% for the cylinders, trilobes, and quadlobes particles, respectively, while the liquid holdups were predicted with MSEs of 0.03%, 0.16%, and 0.01% for the cylinders, trilobes, and quadlobes particles, respectively.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".