Computational modeling of UV photocatalytic reactors: model development, evaluation, and application
Bibliographic record
Abstract
A computational model for simulating the performance of immobilized photocatalytic ultraviolet (UV) reactors used for water treatment was developed, experimentally evaluated, and applied to reactor design optimization. This model integrated hydrodynamics, species mass transport, chemical reaction kinetics, and irradiance distribution within the reactor. Among different hydrodynamic models evaluated against experimental data, the laminar, Abe–Kondoh–Nagano, and Reynolds stress turbulence models showed better performance (errors <5%, 12%, and 20%, respectively) in terms of external mass transfer and surface reaction prediction capabilities at different hydrodynamic conditions. A developed finite-volume-based UV lamp emission model was able to predict, with errors of less than 5%, near- and far-field irradiance measurements. Combining all these models, the integrated computational fluid dynamics (CFD)-based model was able to successfully predict the photocatalytic degradation rate of model pollutants (benzoic acid and 2,4-D) in various configurations of annular reactors and UV lamp sizes, over a wide range of hydrodynamic conditions (350 < Re < 11,000). In addition, the integrated model was used in combination with a Taguchi design of experiments method to perform reactor design optimization. Following this approach, a base case annular reactor design was modified to obtain a 50% more efficient design.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".