Application of response surface methodology and artificial neural network for optimizing phosphate removal from lagoon wastewater
Bibliographic record
Abstract
Lagoons are primary wastewater treatment methods used in rural municipalities and small communities in Canada. This study aimed to optimize phosphate removal and reduce generated sludge in lagoon wastewater treatment under varying operational parameters. Two series of bench scale experiments were conducted to evaluate phosphate removal and sludge production using aluminum sulphate and ferric chloride as coagulants, and cationic polymers as coagulant aids. Two models were developed to predict optimal conditions for phosphate removal and sludge production. Response Surface Methodology (RSM) with optimal design was employed to assess the impact of pH, temperature, coagulant dosage and type, and flocculant type and dosage on the responses. Subsequently, a feedforward multilayer Artificial Neural Network (ANN) model was developed based on RSM inputs, along with floc perimeter and area, to forecast final phosphate levels in effluent and the amount of generated sludge. The results revealed that polymers with 40% cationic charge and higher molecular weight were more efficient compared to polymers with higher charge and lower molecular weights. Additionally, the R-squared (R2 ) values for the RSM models were 0.9264 and 0.9194 for phosphate removal and sludge production, respectively. The corresponding R2 values for the ANN models were 0.7994 and 0.7965, indicating good predictive performance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".