Modelling and optimization of the ferrous to ferric sulphate conversion with hydrogen peroxide using <scp>polynomial‐PSO</scp> and <scp>PSO‐ANNs</scp> models
Bibliographic record
Abstract
Abstract In this present work, polynomial and artificial neural networks models, adjusted by particle swarm optimization, were implemented in an attempt to improve process modelling and to achieve reliable reaction representation behaviour. Parameter optimization of the conversion reaction of ferrous to ferric sulphate in concentrated solutions using hydrogen peroxide as an oxidant is a process of interest due to the importance of producing this ferric salt, a widely used coagulant/flocculant in water treatment, as a chlorine‐free product. Previous work reported a process optimization attempt based on a factorial design of experiments. The obtained polynomial model based on least‐squares showed a minimally satisfactory R 2 of 0.7481. The comparison of the obtained models' performance showed a significant improvement in the prediction of experimental conditions. The results indicated that artificial neural networks‐based models presented a higher predictive capability for highly non‐linear experimental data, and the best model achieved an R 2 of 0.9744 for the conversion prediction. Optimal ranges for cost‐effective process conditions were investigated through the refined response surface charts obtained.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".