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<p>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 conditions (C₀ &gt; 50 mg/L and pH 6–9), a higher adsorption capacity could achieved. A Python-based GUI incorporating CatBoost facilitates practical applications in designing efficient biochar adsorption systems. By merging advanced ML techniques and interpretability tools, this study deepens understanding of biochar’s ammonia adsorption and supports sustainable strategies for mitigating nitrogen pollution.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年2月22日

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  • Chong Liu,
  • Paramasivan Balasubramanian,
  • Jingxian An,
  • Fayong Li

摘要

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 conditions (C₀ > 50 mg/L and pH 6–9), a higher adsorption capacity could achieved. A Python-based GUI incorporating CatBoost facilitates practical applications in designing efficient biochar adsorption systems. By merging advanced ML techniques and interpretability tools, this study deepens understanding of biochar’s ammonia adsorption and supports sustainable strategies for mitigating nitrogen pollution.