Artificial neural networks and genetic algorithms: An efficient modelling and optimization methodology for active chlorine production using the electrolysis process
Bibliographic record
Abstract
Abstract This study evaluates the effectiveness of a modelling and optimization methodology based on artificial neural networks and genetic algorithms in the prediction of the behaviour of an electrolysis process of active chlorine production from a synthetic saline effluent. Multilayer perceptrons feedforward neural networks were developed for the active chlorine production and energy consumption based on the following inputs: electrolysis time, current intensity, hydrochloric acid concentration, and chloride ion concentration. In order to diagnose and prevent the over‐fitting problem during the learning process, learning curves and the regularization factor were utilized. The trained ANN models were able to successfully predict the active chlorine production and energy consumption of the process (R 2 = 0.979 and MSE = 3.826 for active chlorine production and R 2 = 0.985 and MSE = 6.952 for energy consumption). Multi‐objective optimization for maximizing active chlorine production and minimizing energy consumption was carried out by a genetic algorithm using the best derived ANN models. The Pareto front obtained led to multiple non‐dominated optimal points, which result in insights regarding the optimal operating conditions for the process.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".