Determination of Kinetic Parameter in a Unified Kinetic Model for the Photodegradation of Phenol by Using Nonlinear Regression and the Genetic Algorithm
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".