Modeling of Textile Dye Removal from Wastewater Using Innovative Oxidation Technologies (Fe(II)/Chlorine and H<sub>2</sub>O<sub>2</sub>/Periodate Processes): Artificial Neural Network-Particle Swarm Optimization Hybrid Model
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Bibliographic record
Abstract
An efficient optimization technique based on a metaheuristic and an artificial neural network (ANN) algorithm has been devised. Particle swarm optimization (PSO) and ANN were used to estimate the removal of two textile dyes from wastewater (reactive green 12, RG12, and toluidine blue, TB) using two unique oxidation processes: Fe(II)/chlorine and H2O2/periodate. A previous study has revealed that operating conditions substantially influence removal efficiency. Data points were gathered for the experimental studies that developed our ANN-PSO model. The PSO was used to determine the optimum ANN parameter values. Based on the two processes tested (Fe(II)/chlorine and H2O2/periodate), the proposed hybrid model (ANN-PSO) has been demonstrated to be the most successful in terms of establishing the optimal ANN parameters and brilliantly forecasting data for RG12 and TP elimination yield with the coefficient of determination (R2) topped 0.99 for three distinct ratio data sets.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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