Computational fluid dynamics modeling of immobilized photocatalytic reactors for water treatment
Bibliographic record
Abstract
Abstract A computational fluid dynamics (CFD) model for the simulation of immobilized photocatalytic reactors used for water treatment was developed and evaluated experimentally. The model integrated hydrodynamics, species mass transport, chemical reaction kinetics, and irradiance distribution within the reactor. The experimental evaluation was performed using various configurations of annular reactors and ultraviolet lamp sizes over a wide range of hydrodynamic conditions (350 < Re < 11,000). The evaluation showed that the developed CFD model was able to successfully predict the photocatalytic degradation rate of a model pollutant in the analyzed reactors. In terms of hydrodynamic models, the results demonstrated that the laminar model performs well for systems under laminar flow conditions, whereas the Abe‐Kondoh‐Nagano low Reynolds number and the Reynolds stress turbulence models give accurate predictions for photoreactors under transitional or turbulent flow regimes. The performed analysis confirmed that degradation rates of organic contaminants in immobilized photocatalytic reactors are strongly limited by external mass transfer; as a consequence, the degradation prediction capability of the CFD model is largely determined by the external mass transfer prediction performance of the hydrodynamic models used. © 2010 American Institute of Chemical Engineers AIChE J, 2011
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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 | 0.000 |
| 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 | 0.000 |
| 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".