CFD simulation of UV photocatalytic reactors for air treatment
Bibliographic record
Abstract
Abstract A photocatalytic reactor was simulated through computational fluid dynamics (CFD) with surface reaction for trichloroethylene (TCE) oxidation at various pollutant concentrations, flow rates, and reactor lengths. The results were compared with those from experiments. The experimental work involved using a differential photoreactor for kinetics studies and an annular flow photoreactor for overall removal investigations under various conditions. The modeling predictions agreed closely with the experimental data within the range in which results were examined. The modeling results indicated significant radial TCE concentration gradient and nonuniform flow distributions in the annular photoreactor. CFD was applied to predict the performance of a number of UV photocatalytic reactor design concepts, to study the impacts of some design parameters on the reactor efficiency. The modeling results demonstrated that under similar flow rate conditions, the thickness of the contaminated air layer flowing over the photocatalyst surface could substantially influence the reactor performance. Thinner contaminated air layers provided more uniform radial concentration distribution of TCE and improved the reactor performance. © 2005 American Institute of Chemical Engineers AIChE J, 2005
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".