A systematic review on machine learning-aided design of engineered biochar for soil and water contaminant removal
The design and application of engineered biochar is crucial for removing contaminants from soil and water,yet its development and commercialization still depend on time- and labor-intensive experimental methods. Machine learning (ML) offers a faster alternative, but despite its growing use in biocha...
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| Format: | Article |
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Soil Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fsoil.2025.1623083/full |
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| author | Yunpeng Ge Kaiyang Ying Guo Yu Muhammad Ubaid Ali Abubakr M. Idris Abubakr M. Idris Asfandyar Shahab Habib Ullah Habib Ullah |
| author_facet | Yunpeng Ge Kaiyang Ying Guo Yu Muhammad Ubaid Ali Abubakr M. Idris Abubakr M. Idris Asfandyar Shahab Habib Ullah Habib Ullah |
| author_sort | Yunpeng Ge |
| collection | DOAJ |
| description | The design and application of engineered biochar is crucial for removing contaminants from soil and water,yet its development and commercialization still depend on time- and labor-intensive experimental methods. Machine learning (ML) offers a faster alternative, but despite its growing use in biochar research, no review systematically covers ML-driven design of engineered biochar for large-scale contaminant removal. This work fills that gap by analyzing ML’s role in optimizing biochar properties using pilot and industrial-scale datal. We examine key biochar characteristics, including physical (e.g., surface area, pore volume), chemical (e.g., ultimate/proximate analysis, aromatization), electrochemical (e.g., cation exchange capacity, electrical conductivity), and functional group properties, and their optimization for various contaminants. With special attention on three mechanistic dimensions, this review offers the first thorough study of ML applications for designing biochars based on pilot and industrial-scale data: ML forecasts micropore-mesopore synergies controlling diffusion-limited adsorption of heavy metals (Pb²+, Cd²+); surface chemistry optimization - including oxygen functional group (-COOH, -OH); and electrochemical tuning - of redox-active sites for contaminant transformation. The paper emphasizes how ML models—such as Random Forest (RF) and Gradient Boosting Regression (GBR)—elucidate the nonlinear links between pyrolysis conditions (temperature, feedstock composition) and biochar performance. For adsorption, surface area and pore volume are distinctly important; in redox reactions for heavy metal removal, functional groups like C-O and C=O play vital roles. Unlike earlier studies mostly on the adsorption capacity of biochar, this work expands the scope to investigate how ML can customize biochar properties for optimal contaminant removal using interpretability tools like SHAP analysis. These instruments expose parameters including nitrogen-to-carbon (N/C) ratios and pyrolysis temperature in adsorption efficiency. The review also covers hybrid methods combining ML with molecular simulations (e.g., DFT) to link mechanistic knowledge with data-driven predictions. Emphasizing the need for multidisciplinary collaboration, the review finally shows future directions for ML-driven biochar design, guiding fieldwork by pointing out shortcomings of present techniques and opportunities for ML. |
| format | Article |
| id | doaj-art-5f15aab4c4804e96aa360e8ffd5440c1 |
| institution | Matheson Library |
| issn | 2673-8619 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Soil Science |
| spelling | doaj-art-5f15aab4c4804e96aa360e8ffd5440c12025-07-03T05:26:38ZengFrontiers Media S.A.Frontiers in Soil Science2673-86192025-07-01510.3389/fsoil.2025.16230831623083A systematic review on machine learning-aided design of engineered biochar for soil and water contaminant removalYunpeng Ge0Kaiyang Ying1Guo Yu2Muhammad Ubaid Ali3Abubakr M. Idris4Abubakr M. Idris5Asfandyar Shahab6Habib Ullah7Habib Ullah8Guangxi Key Laboratory of Calcium Carbonate Resources Comprehensive Utilization, College of Materials and Chemical Engineering, Hezhou University, Hezhou, ChinaCollege of Environmental Science and Engineering, Guilin University of Technology, Guilin, ChinaCollege of Environmental Science and Engineering, Guilin University of Technology, Guilin, ChinaCollege of Chemical Engineering, Huaqiao University, Xiamen, ChinaDepartment of Chemistry, College of Science, King Khalid University, Abha, Saudi ArabiaResearch Center for Advanced Materials Science (RCAMS), King Khalid University, Abha, Saudi ArabiaSchool of Environmental Science and Engineering, Hainan University, Haikou, ChinaDepartment of Environmental Science, Zhejiang University, Hangzhou, Zhejiang, ChinaInnovation Center of Yangtze River Delta, Zhejiang University, Jiashan, ChinaThe design and application of engineered biochar is crucial for removing contaminants from soil and water,yet its development and commercialization still depend on time- and labor-intensive experimental methods. Machine learning (ML) offers a faster alternative, but despite its growing use in biochar research, no review systematically covers ML-driven design of engineered biochar for large-scale contaminant removal. This work fills that gap by analyzing ML’s role in optimizing biochar properties using pilot and industrial-scale datal. We examine key biochar characteristics, including physical (e.g., surface area, pore volume), chemical (e.g., ultimate/proximate analysis, aromatization), electrochemical (e.g., cation exchange capacity, electrical conductivity), and functional group properties, and their optimization for various contaminants. With special attention on three mechanistic dimensions, this review offers the first thorough study of ML applications for designing biochars based on pilot and industrial-scale data: ML forecasts micropore-mesopore synergies controlling diffusion-limited adsorption of heavy metals (Pb²+, Cd²+); surface chemistry optimization - including oxygen functional group (-COOH, -OH); and electrochemical tuning - of redox-active sites for contaminant transformation. The paper emphasizes how ML models—such as Random Forest (RF) and Gradient Boosting Regression (GBR)—elucidate the nonlinear links between pyrolysis conditions (temperature, feedstock composition) and biochar performance. For adsorption, surface area and pore volume are distinctly important; in redox reactions for heavy metal removal, functional groups like C-O and C=O play vital roles. Unlike earlier studies mostly on the adsorption capacity of biochar, this work expands the scope to investigate how ML can customize biochar properties for optimal contaminant removal using interpretability tools like SHAP analysis. These instruments expose parameters including nitrogen-to-carbon (N/C) ratios and pyrolysis temperature in adsorption efficiency. The review also covers hybrid methods combining ML with molecular simulations (e.g., DFT) to link mechanistic knowledge with data-driven predictions. Emphasizing the need for multidisciplinary collaboration, the review finally shows future directions for ML-driven biochar design, guiding fieldwork by pointing out shortcomings of present techniques and opportunities for ML.https://www.frontiersin.org/articles/10.3389/fsoil.2025.1623083/fullmachine learning (ML)engineered biocharenvironmental remediationadsorptioncontaminants |
| spellingShingle | Yunpeng Ge Kaiyang Ying Guo Yu Muhammad Ubaid Ali Abubakr M. Idris Abubakr M. Idris Asfandyar Shahab Habib Ullah Habib Ullah A systematic review on machine learning-aided design of engineered biochar for soil and water contaminant removal Frontiers in Soil Science machine learning (ML) engineered biochar environmental remediation adsorption contaminants |
| title | A systematic review on machine learning-aided design of engineered biochar for soil and water contaminant removal |
| title_full | A systematic review on machine learning-aided design of engineered biochar for soil and water contaminant removal |
| title_fullStr | A systematic review on machine learning-aided design of engineered biochar for soil and water contaminant removal |
| title_full_unstemmed | A systematic review on machine learning-aided design of engineered biochar for soil and water contaminant removal |
| title_short | A systematic review on machine learning-aided design of engineered biochar for soil and water contaminant removal |
| title_sort | systematic review on machine learning aided design of engineered biochar for soil and water contaminant removal |
| topic | machine learning (ML) engineered biochar environmental remediation adsorption contaminants |
| url | https://www.frontiersin.org/articles/10.3389/fsoil.2025.1623083/full |
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