Machine learning prediction of ammonia nitrogen adsorption on biochar with model evaluation and optimization
Abstract 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...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | npj Clean Water |
| Online Access: | https://doi.org/10.1038/s41545-024-00429-z |
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| _version_ | 1850190795654561792 |
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| author | Chong Liu Paramasivan Balasubramanian Jingxian An Fayong Li |
| author_facet | Chong Liu Paramasivan Balasubramanian Jingxian An Fayong Li |
| author_sort | Chong Liu |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-2e980760c4a74365ae55cbcd98eb65e1 |
| institution | OA Journals |
| issn | 2059-7037 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Clean Water |
| spelling | doaj-art-2e980760c4a74365ae55cbcd98eb65e12025-08-20T02:15:11ZengNature Portfolionpj Clean Water2059-70372025-02-018111210.1038/s41545-024-00429-zMachine learning prediction of ammonia nitrogen adsorption on biochar with model evaluation and optimizationChong Liu0Paramasivan Balasubramanian1Jingxian An2Fayong Li3Department of Chemical & Materials Engineering, University of AucklandDepartment of Biotechnology & Medical Engineering, National Institute of Technology RourkelaDepartment of Chemical & Materials Engineering, University of AucklandCollege of Water Resources and Architectural Engineering, Tarim UniversityAbstract 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.https://doi.org/10.1038/s41545-024-00429-z |
| spellingShingle | Chong Liu Paramasivan Balasubramanian Jingxian An Fayong Li Machine learning prediction of ammonia nitrogen adsorption on biochar with model evaluation and optimization npj Clean Water |
| title | Machine learning prediction of ammonia nitrogen adsorption on biochar with model evaluation and optimization |
| title_full | Machine learning prediction of ammonia nitrogen adsorption on biochar with model evaluation and optimization |
| title_fullStr | Machine learning prediction of ammonia nitrogen adsorption on biochar with model evaluation and optimization |
| title_full_unstemmed | Machine learning prediction of ammonia nitrogen adsorption on biochar with model evaluation and optimization |
| title_short | Machine learning prediction of ammonia nitrogen adsorption on biochar with model evaluation and optimization |
| title_sort | machine learning prediction of ammonia nitrogen adsorption on biochar with model evaluation and optimization |
| url | https://doi.org/10.1038/s41545-024-00429-z |
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