Determination of Kinetic Parameter in a Unified Kinetic Model for the Photodegradation of Phenol by Using Nonlinear Regression and the Genetic Algorithm
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Bibliographic record
Abstract
Abstract This study reports the kinetic parameter estimation in the photocatalytic degradation of phenol over different TiO 2 catalysts by using the Genetic Algorithm (GA) and nonlinear regression. Reaction networks are based on a previously reported unified kinetic model (UKM) of the Langmuir–Hinshelwood type. Nonlinear least-squares fitting and GA are used to find the values for the kinetic constants. The computed parameters were found to predict experimental data for phenol photodegradation at different levels of concentrations. It is shown that both methods render close values for the kinetic constants. This suggests that UKM approach gives the global minimum and as a result, this method provides good and objective parameter estimates with low to moderate cross-correlation among kinetic constants and acceptable 95% Confidence Intervals (CIs). Global optimization by using GA requires extensive computer times of up to 5 minutes. Least square fitting provides the same results with computer times of seconds only. It is then concluded that the UKM approach effectively avoids overparameterization by finding the global optimum when optimizing the kinetic constants.
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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 | 0.000 | 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 | 0.000 |
| 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 it