Deciphering Phosphorus Recovery from Wastewater via Machine Learning: Comparative Insights Among Al3+, Fe3+ and Ca2+ Systems

Efficient phosphorus recovery is of great significance for sustainable wastewater management and resource recycling. While chemical precipitation is widely used, its effectiveness under complex multi-factor conditions remains challenging to predict and optimize. This study compiled a multidimensional dataset from recent experimental literature, encompassing key operational parameters (reaction time, temperature, pH, stirring speed) and dosages of three metal precipitants (Al3+, Ca2+, Fe3+) to systematically evaluate and benchmark phosphorus recovery performance across these distinct systems, six machine learning algorithms—Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Gaussian Process Regression (GPR), Elastic Net, Artificial Neural Network (ANN), and Partial Least Squares Regression (PLSR)—were developed and cross-validated. Among them, the GPR model exhibited superior predictive accuracy and robustness. (R2 = 0.69, RMSE = 0.54). Beyond achieving high-fidelity predictions, this study advances the field by integrating interpretability analysis with Shapley Additive Explanations (SHAP) and Partial Dependence Plots (PDP). These analyses identified distinct controlling factors across systems: reaction time and pH for aluminum, Ca2+ dosage and alkalinity for calcium, and phosphorus loading with stirring speed for iron. The revealed factor-specific mechanisms and synergistic interactions (e.g., among pH, metal dose, and mixing intensity) provide actionable insights that transcend black-box prediction. This work presents an interpretable Machine Learning (ML) framework that offers both theoretical insights and practical guidance for optimizing phosphorus recovery in multi-metal systems and enabling precise control in wastewater treatment operations.