Modeling of Degradation of Diazo Dye in Swirl-Flow Photocatalytic Reactor: Response Surface Approach
Bibliographic record
Abstract
Photocatalytic degradation of Direct Blue 15 (DB15), an azo dye, was studied using a swirl-flow monolithic reactor under UV irradiation. The degradation reactions were carried out to investigate effects of initial dye concentration, catalyst loading, and light intensity at an optimal pH. The experiments were designed and mathematically modelled by CCD-RSM (central composite design-response surface methodology) approach. It was found that the selected parameters significantly affect DB15 degradation. In terms of the linear term, catalyst loading and light intensity had a synergistic effect, while dye concentration registered the opposite effect. Strong interaction was observed between catalyst loading and both light intensity and initial dye concentration compared with the interaction of light intensity and initial dye concentration. Based on the experimental results, a quadratic model was developed to predict the percentage removal of DB15. The predicted values of the model were in good agreement with the experimental values (R2 = 0.987), indicating the model fits well for the parameter space for which experiments were performed. According to diagnostic plots, the model credibility was valid because its residuals were distributed normally and exhibited a random pattern based on their examination versus the predicted values. The results revealed that the initial dye concentration and catalyst concentration have a significant effect on the mineralization time.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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".