Correlation and prediction of azo dye degradation by nonlinear least-square regression in combined ozonation and ultrasonolysis processes
Bibliographic record
Abstract
Correlative reaction kinetics based on nonlinear least square regression were obtained to predict the degradation of p-aminophenol (PAP), C.I. Reactive Yellow 84 (RY84), C.I. Reactive Blue 19 (RB19), and C.I. Direct Red 23 (DR23) in combined ultrasonolysis and ozonation. Effects of ultrasonolysis and ozonation as individual processes, as well as the synergetic effect for the degradation of azo dyes, were studied by linear and nonlinear regression methods. An initial dye concentration correlation index (φ) for each compound was developed to study the effects of initial concentrations of organics on their reaction rate constants. Reaction rate constants for the azo dyes’ degradation in ozonation and ultrasonolysis alone and their combination were obtained for different operating conditions. Results showed that ultrasonolysis alone was almost ineffective for decolorization and mineralization of azo dyes, but the combination of ozonation and ultrasonolysis had a significant effect on their degradation. Both ozone dose and ultrasonic density had a direct effect on the reaction rate constants, where the impact of ozone was greater with respect to that of the ultrasound. The synergetic effect of combined processes as a function of ozone concentration and ultrasound density varied from 0 to 5, 0 to 4, and 0 to 35 for RY84, RB19, and DR23, respectively.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".