Feature-engineered machine learning for daily-scale prediction of effluent total phosphorus and coagulant dosing optimization in full-scale DAF systems

= 0.818; RMSE = 0.032 mg/L), corresponding to a prediction error within 20% of the discharge limit (0.2 mg/L). SHAP analysis identified influent T-P, coagulant dosage, and short-term variation as dominant drivers across seasons. A sensitivity-based autoregressive simulation indicated that optimized dosing could reduce coagulant consumption by 32-51%, yielding an estimated annual cost saving of 1.53 billion KRW while improving effluent stability. The proposed framework demonstrates the practical value of daily-scale, interpretable machine learning for data-driven DAF operation.