Deposition and aggregation of Brownian particles in trickle‐bed reactors
Bibliographic record
Abstract
Abstract When dilute liquid suspensions contaminated with Brownian fine solids are treated in catalytic trickle‐bed reactors, bed plugging develops and increases the resistance to two‐phase flow until ultimate unit shutdown for bed substitution with pristine catalyst is imposed. One of the important aspects during plugging with Brownian particles is the aggregation of fines and the release of the fine particles and aggregates from pore bodies within the porous bed as a result of the hydrodynamic or colloidal forces. Current physical models linking gas–liquid flow to the filtration process in high‐pressure/temperature trickle beds neglect the possible colloidal particle aggregation and the release of aggregates. This work attempts to fill this gap by developing a Euler–Euler fluid dynamic model based on the volume average mass, momentum, and species balance equations, filtration equations for the Brownian particles and the aggregates, and the discrete population balance equations for the agglomeration of particles. Both monolayer and multilayer depositions were considered for Brownian particles and only the monolayer deposition in the case of the detaching aggregates. The release of fine particles and aggregates from the collector surface was assumed to be induced by the colloidal forces in the case of Brownian particles/aggregates or by the hydrodynamic forces in the case of non‐Brownian aggregates. Brownian particle aggregation was described by the rate at which a certain size aggregate is being formed by smaller aggregates less the rate at which the aggregate combines to form a larger aggregate. © 2006 American Institute of Chemical Engineers AIChE J 2006
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".