Modeling of an aerosol reactor for optimizing product properties
Bibliographic record
Abstract
Abstract Important and unique metal powders are made industrially by a variety of vapor condensation processes in tube reactors. Often, however, the fundamental mechanisms for particle formation and growth are still not well understood. In this article, a computational fluid dynamics (CFD) model was developed to examine a tube reactor's internal flow characteristics. The model identified a massive zone of fluid recirculation in the top half of the reactor. In‐situ sampling from an experimental reactor under the same conditions revealed a large increase of aerosol particle size corresponding to the region of recirculation. A first principles mass balance model based on chemical kinetics and aerosol physics was developed for this system which showed that the average particle size grew monotonically with time in the reactor. On the basis of this firmly established link between residence time and particle size, a new reactor geometry was proposed to produce a “plug‐flow” velocity profile with a narrower particle size distribution. A CFD model was used to prototype the new configuration, and then this new reactor design was tested experimentally to confirm that the design objective was achieved. This work shows the potential synergies between first principles models for process understanding and CFD models for process prototyping and optimization. © 2007 American Institute of Chemical Engineers AIChE J, 2007
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".