Machine Learning Based Alum Dosing Optimization for Adaptive Water Quality Management in Treatment Plant

Ensuring safe and cost-effective water purification remains a critical challenge, particularly for large natural water bodies like the Halda River, where water quality parameters fluctuate significantly. Traditional methods for determining alum dosages often rely on manual experiments that fail to adapt to real-time variations, leading to inefficiencies and chemical overuse. This study presents a data-driven solution to optimize alum dosage prediction using advanced machine learning techniques. A comprehensive dataset comprising raw and treated water quality parameters including turbidity, pH, alkalinity, and chloride from the Halda River was utilized. Multiple models were trained including Random Forest, Gradient Boosting, Support Vector Machine, Logistic Regression, and K-Nearest Neighbors. The KNN model achieved the highest performance with a test accuracy of 94.87% and an ROC AUC of 0.957. Feature importance analysis revealed that turbidity, chloride concentration, and pH-related interactions are the most influential predictors of alum demand. The developed model offers a practical framework for real-time automated alum dosage recommendations, enabling water treatment facilities to reduce operational costs, minimize chemical waste, and improve treatment consistency.