Application of response surface methodology and artificial neural network for optimizing phosphate removal from lagoon wastewater
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Date
2024-07-24
Authors
Khordadi, Aidin
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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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Keywords
Lagoon wastewater, Phosphorus removal, RSM and ANN