Nonlinear Modeling for the Degradation of Aqueous Azo Dyes by Combined Advanced Oxidation Processes Using Artificial Neural Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
One-hidden-layer artificial neural networks (ANNs) using a back-propagation structure have been trained on different sets of experimental data to identify and evaluate the degradation of different azo dyes (Reactive Yellow 84, Reactive Blue 19, Direct Red 23, Direct Red 28, and Acid Blue 193) by photo-Fenton process and combined ozonation and ultrasonolysis processes. Different input variables such as pH, initial concentrations of dyes and ozone, reaction time, ultrasonic power density, and initial concentrations of hydrogen peroxide and ferrous in aqueous solution were employed to model the degradation rates of azo dyes based on the decolorization efficiency and the removal rate using chemical oxygen demand (COD) and total organic carbon (TOC). A new model expression is developed to find the effect of individual parameters and their interactions on the efficiency of organic degradation by advanced oxidation processes.
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.
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