Machine learning prediction of ammonia nitrogen adsorption on biochar with model evaluation and optimization

In light of escalating nitrogen pollution in aquatic systems, this study presents a comprehensive machine learning (ML) approach to predict ammonia nitrogen adsorption capacity of biochar and identify optimal conditions. Twelve ML models, including tree-based ensembles, kernel-based methods, and deep learning, were evaluated using Bayesian optimization and cross-validation. Results show tree-based ensemble models excel, with CatBoost performing best (R² = 0.9329, RMSE = 0.5378) and demonstrating strong generalization. Using SHAP and Partial Dependence Plots, we found experimental conditions (67.2%) and biochar’s chemical properties (18.2%) most influenced adsorption capacity. Moreover, under these experimental…

• NN
  National Natural Science Foundation of China

  Award: 42275014