November 30, 2025Open Access

Machine Learning-Based Prediction of Coagulant Dosing in Drinking Water Treatment Plants Using Polynomial Regression with Lasso Regularization

JAJusuk An JPJoonhong Park SYSeungjae Yeon

Key Points

Lasso regularization improved prediction accuracy, achieving R2 = 0.951 in drinking water treatment plants.
Model outperformed traditional linear regression, with MAPE reduced to 7.02% and stable generalization at high polynomial degrees.
Framework supports real-time control under nonlinear interactions, enhancing operator understanding of dosing behavior.
Study indicates operationally transferable insights for sustainable automation and efficient water treatment operations.

Abstract

Coagulation is a critical unit process in drinking water treatment plants (DWTPs), where accurate dosing of coagulants such as polyaluminum chloride (PAC) and polyaluminum hydroxide chloride silicate (PACS) directly determines turbidity removal and operational stability. However, nonlinear interactions among water-quality variables complicate dosage prediction, and jar tests or operator heuristics cannot support real-time control. This study presents a scientifically interpretable and operationally transferable framework based on polynomial multiple linear regression (PMLR) with Lasso regularization, which was specifically developed for full-scale DWTP environments. While conventional PMLR rapidly overfits beyond polynomial degrees of 4–5, the Lasso-regularized model maintained stable generalization even at a degree of 10 by automatically pruning redundant terms and suppressing multicollinearity, thereby minimizing the need for manual hyperparameter tuning. Using 8303 hourly operational records from a full-scale DWTP in Korea, the Lasso-PMLR achieved R2 = 0.951, RMSE = 0.120, and MAPE = 7.02%, outperforming traditional linear regression (R2 = 0.896; MAPE = 8.64%). This proportional stability across increasing polynomial degrees, demonstrated directly using long-term real-world data, is particularly valuable for practical deployment because it ensures robustness without complex model-selection procedures. The transparent coefficient structure enables operators—who typically rely on jar tests—to understand and adjust dosing behavior, offering a field-ready and interpretable alternative to black-box models and supporting more efficient coagulant use, reduced sludge production, and sustainable automation in DWTP operation.

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