Synthesis optimization and adsorption modeling of biochar for pollutant removal via machine learning
Abstract Due to large specific surface area, abundant functional groups and low cost, biochar is widely used for pollutant removal. The adsorption performance of biochar is related to biochar synthesis and adsorption parameters. But the influence factor is numerous, the traditional experimental enum...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2023-04-01
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| Series: | Biochar |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s42773-023-00225-x |
| _version_ | 1797840859922694144 |
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| author | Wentao Zhang Ronghua Chen Jie Li Tianyin Huang Bingdang Wu Jun Ma Qingqi Wen Jie Tan Wenguang Huang |
| author_facet | Wentao Zhang Ronghua Chen Jie Li Tianyin Huang Bingdang Wu Jun Ma Qingqi Wen Jie Tan Wenguang Huang |
| author_sort | Wentao Zhang |
| collection | DOAJ |
| description | Abstract Due to large specific surface area, abundant functional groups and low cost, biochar is widely used for pollutant removal. The adsorption performance of biochar is related to biochar synthesis and adsorption parameters. But the influence factor is numerous, the traditional experimental enumeration is powerless. In recent years, machine learning has been gradually employed for biochar, but there is no comprehensive review on the whole process regulation of biochar adsorbents, covering synthesis optimization and adsorption modeling. This review article systematically summarized the application of machine learning in biochar adsorbents from the perspective of all-round regulation for the first time, including the synthesis optimization and adsorption modeling of biochar adsorbents. Firstly, the overview of machine learning was introduced. Then, the latest advances of machine learning in biochar synthesis for pollutant removal were summarized, including prediction of biochar yield and physicochemical properties, optimal synthetic conditions and economic cost. And the application of machine learning in pollutant adsorption by biochar was reviewed, covering prediction of adsorption efficiency, optimization of experimental conditions and revelation of adsorption mechanism. General guidelines for the application of machine learning in whole-process optimization of biochar from synthesis to adsorption were presented. Finally, the existing problems and future perspectives of machine learning for biochar adsorbents were put forward. We hope that this review can promote the integration of machine learning and biochar, and thus light up the industrialization of biochar. Graphical Abstract |
| first_indexed | 2024-04-09T16:21:37Z |
| format | Article |
| id | doaj.art-7743ce6a4ac14bb5acfe2c3ec7ec298a |
| institution | Directory Open Access Journal |
| issn | 2524-7867 |
| language | English |
| last_indexed | 2024-04-09T16:21:37Z |
| publishDate | 2023-04-01 |
| publisher | Springer |
| record_format | Article |
| series | Biochar |
| spelling | doaj.art-7743ce6a4ac14bb5acfe2c3ec7ec298a2023-04-23T11:24:34ZengSpringerBiochar2524-78672023-04-015112510.1007/s42773-023-00225-xSynthesis optimization and adsorption modeling of biochar for pollutant removal via machine learningWentao Zhang0Ronghua Chen1Jie Li2Tianyin Huang3Bingdang Wu4Jun Ma5Qingqi Wen6Jie Tan7Wenguang Huang8Tsinghua Shenzhen International Graduate School, Tsinghua UniversityMinistry of Ecology and Environment of PRC, South China Institute of Environmental SciencesSchool of Chemical Engineering, Beijing University of Chemical TechnologySchool of Environmental Science and Engineering, Suzhou University of Science and TechnologySchool of Environmental Science and Engineering, Suzhou University of Science and TechnologyMinistry of Ecology and Environment of PRC, South China Institute of Environmental SciencesMinistry of Ecology and Environment of PRC, South China Institute of Environmental SciencesMinistry of Ecology and Environment of PRC, South China Institute of Environmental SciencesMinistry of Ecology and Environment of PRC, South China Institute of Environmental SciencesAbstract Due to large specific surface area, abundant functional groups and low cost, biochar is widely used for pollutant removal. The adsorption performance of biochar is related to biochar synthesis and adsorption parameters. But the influence factor is numerous, the traditional experimental enumeration is powerless. In recent years, machine learning has been gradually employed for biochar, but there is no comprehensive review on the whole process regulation of biochar adsorbents, covering synthesis optimization and adsorption modeling. This review article systematically summarized the application of machine learning in biochar adsorbents from the perspective of all-round regulation for the first time, including the synthesis optimization and adsorption modeling of biochar adsorbents. Firstly, the overview of machine learning was introduced. Then, the latest advances of machine learning in biochar synthesis for pollutant removal were summarized, including prediction of biochar yield and physicochemical properties, optimal synthetic conditions and economic cost. And the application of machine learning in pollutant adsorption by biochar was reviewed, covering prediction of adsorption efficiency, optimization of experimental conditions and revelation of adsorption mechanism. General guidelines for the application of machine learning in whole-process optimization of biochar from synthesis to adsorption were presented. Finally, the existing problems and future perspectives of machine learning for biochar adsorbents were put forward. We hope that this review can promote the integration of machine learning and biochar, and thus light up the industrialization of biochar. Graphical Abstracthttps://doi.org/10.1007/s42773-023-00225-xBiocharPyrolysisAdsorptionPollutant removalMachine learningArtificial intelligence |
| spellingShingle | Wentao Zhang Ronghua Chen Jie Li Tianyin Huang Bingdang Wu Jun Ma Qingqi Wen Jie Tan Wenguang Huang Synthesis optimization and adsorption modeling of biochar for pollutant removal via machine learning Biochar Biochar Pyrolysis Adsorption Pollutant removal Machine learning Artificial intelligence |
| title | Synthesis optimization and adsorption modeling of biochar for pollutant removal via machine learning |
| title_full | Synthesis optimization and adsorption modeling of biochar for pollutant removal via machine learning |
| title_fullStr | Synthesis optimization and adsorption modeling of biochar for pollutant removal via machine learning |
| title_full_unstemmed | Synthesis optimization and adsorption modeling of biochar for pollutant removal via machine learning |
| title_short | Synthesis optimization and adsorption modeling of biochar for pollutant removal via machine learning |
| title_sort | synthesis optimization and adsorption modeling of biochar for pollutant removal via machine learning |
| topic | Biochar Pyrolysis Adsorption Pollutant removal Machine learning Artificial intelligence |
| url | https://doi.org/10.1007/s42773-023-00225-x |
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