Tree-Based Machine Learning Models for Predicting Phosphate Adsorption Performance of Layered Double Hydroxides: Insights Into Mechanisms and Process Optimization.

Optimizing layered double hydroxides (LDHs) for phosphate removal is challenged by complex, multivariable interactions. To bridge this knowledge gap, this study introduces a novel data-driven framework leveraging a comprehensive 2251-record dataset and advanced machine learning to predict and optimize LDHs adsorption capacity. Among six tree-based models evaluated, CatBoost achieved the best overall performance across five random seeds, with a test R2 of 0.9918 ± 0.0020 and a cross-validation root-mean-square error (RMSE) of 6.114 ± 1.282. Crucially, by integrating SHAP, partial dependence plots, and feature importance, we decoded the combined effects of synthesis and environmental factors on phosphate adsorption. The main contribution lies in translating these predictive insights into actionable engineering guidelines, specifically delineating optimal operational windows (e.g., pH 5-8, calcination temperature 400°C-600°C). This work provides a robust methodology for the targeted design of water-treatment adsorbents, accelerating their practical application.