Effects of Liquid and Gas Flow Rates on the Performance of a Fluidized Bed Photocatalytic Reactor
Bibliographic record
Abstract
Titanium dioxide was immobilized onto spherical activated carbon particles via the sol-gel coating method. The photo-catalyst, annealed at 500 °C, was found to posses the highest activity according to the results of a bench-scale test. The photocatalytic performance of the immobilized photocatalyst was studied in a three-phase fluidized bed photoreactor, with the use of phenol as the model pollutant. Effects of both liquid and air flow rates on the phenol degradation rate were examined. The experimental results showed that the increase of liquid and air flow rates may enhance the phenol degradation rate. However, very high liquid and air flow rates could lead to the decrease in the performance of the three-phase fluidized bed photoreactor. The results on the effect of initial phenol concentration on the degradation rate indicated that the photocatalytic reaction in the three-phase fluidized bed followed the first order kinetics and could be reasonably fitted by the Langmiur-Hinshelwood kinetics model. Compared to the three-phase fluidized bed in which air is introduced into the bed from the distributor, the liquid-solids fluidized bed in which oxygen is provided by injecting air into the freeboard region of the reactor showed a better phenol destruction performance and is thus preferred for photodegradation of water contaminants.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".