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Machine learning models, including multiple linear regression (MLR) and decision tree machine learning (LGB) algorithms, were used to predict the degradation efficiency of 57 organic pollutants in the Fenton process. Mechanistic interpretation of the models was performed by SHAP method to identify the key factors affecting the degradation efficiency. The results showed that the LGB model (R2adj = 0.969, Q2LOO = 0.925, R2ext = 0.844) outperformed the MLR model (R2adj = 0.831, Q2LOO = 0.802, R2ext = 0.861) in terms of prediction accuracy. The SHAP analysis revealed that temperature, molecular three-dimensional structure, and atomic ionization capacity were the main factors affecting the degradation efficiency. This study provides a scientific basis for optimizing the operating conditions of the Fenton process and enhancing the degradation efficiency, which is of great significance in guiding the research and practice in the field of water treatment.

, authors=Lin-tang YU, Dong-dong CHEN, Cui-cui TAO, Teng-yi ZHU*, authorsList=Lin-tang YU, Dong-dong CHEN, Cui-cui TAO, Teng-yi ZHU, authorCompany=null, correspAuthors=Teng-yi ZHU, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1314917713357668733, articleId=1314917712824992123, tenantId=1146029695717560320, journalId=1234093305789726721, language=CN, title=基于可解释性机器学习的芬顿工艺降解有机污染物速率的研究, columnId=1234106386565624579, journalTitle=中国环境科学, columnName=水污染与控制, runingTitle=null, highlight=null, articleAbstract=

采用机器学习模型,包括多元线性回归(MLR)和轻量级梯度提升机(LGB)算法,预测芬顿工艺中57种有机污染物的降解效率.通过SHAP方法对模型进行机理解释,识别了影响降解效率的关键因素.研究结果表明,LGB模型在预测精度上(R2adj = 0.969,Q2LOO= 0.925,R2ext= 0.844)优于MLR模型(R2adj=0.831,Q2LOO=0.802,R2ext=0.861).SHAP分析揭示了温度、分子三维结构和原子电离能力是影响降解效率的主要因素.本研究为优化芬顿工艺的操作条件和提升降解效率提供了科学依据,对水处理领域的研究和实践具有重要的指导意义.

, authors=于林堂, 陈咚咚, 陶翠翠, 朱腾义*, authorsList=于林堂, 陈咚咚, 陶翠翠, 朱腾义, authorCompany=null, correspAuthors=朱腾义, authorNote=

于林堂(1974-),男,江苏扬州人,副研究员,硕士,主要从事水污染化学方面研究.发表论文20余篇..

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* 责任作者,教授,
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于林堂(1974-),男,江苏扬州人,副研究员,硕士,主要从事水污染化学方面研究.发表论文20余篇..

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于林堂(1974-),男,江苏扬州人,副研究员,硕士,主要从事水污染化学方面研究.发表论文20余篇..

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基于可解释性机器学习的芬顿工艺降解有机污染物速率的研究
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于林堂 , 陈咚咚 , 陶翠翠 , 朱腾义 *
中国环境科学 | 水污染与控制 2025,45(8): 4294-4302
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中国环境科学 |水污染与控制 2025 , 45 (8) : 4294 -4302
基于可解释性机器学习的芬顿工艺降解有机污染物速率的研究
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于林堂 , 陈咚咚, 陶翠翠, 朱腾义*
作者信息
  • 扬州大学环境科学与工程学院,江苏 扬州 225127
通讯作者:
* 责任作者,教授,
作者简介:

于林堂(1974-),男,江苏扬州人,副研究员,硕士,主要从事水污染化学方面研究.发表论文20余篇..

Research on the degradation rate of organic pollutants in fenton process based on explainable machine learning
Lin-tang YU , Dong-dong CHEN, Cui-cui TAO, Teng-yi ZHU*
Affiliations
  • College of Environmental Science and Engineering, Yangzhou University, Yangzhou 225127, China
出版时间: 2025-08-20
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采用机器学习模型,包括多元线性回归(MLR)和轻量级梯度提升机(LGB)算法,预测芬顿工艺中57种有机污染物的降解效率.通过SHAP方法对模型进行机理解释,识别了影响降解效率的关键因素.研究结果表明,LGB模型在预测精度上(R2adj = 0.969,Q2LOO= 0.925,R2ext= 0.844)优于MLR模型(R2adj=0.831,Q2LOO=0.802,R2ext=0.861).SHAP分析揭示了温度、分子三维结构和原子电离能力是影响降解效率的主要因素.本研究为优化芬顿工艺的操作条件和提升降解效率提供了科学依据,对水处理领域的研究和实践具有重要的指导意义.

芬顿氧化工艺  /  污染物降解速率常数log K  /  有机污染物  /  机器学习模型  /  SHAP分析

Machine learning models, including multiple linear regression (MLR) and decision tree machine learning (LGB) algorithms, were used to predict the degradation efficiency of 57 organic pollutants in the Fenton process. Mechanistic interpretation of the models was performed by SHAP method to identify the key factors affecting the degradation efficiency. The results showed that the LGB model (R2adj = 0.969, Q2LOO = 0.925, R2ext = 0.844) outperformed the MLR model (R2adj = 0.831, Q2LOO = 0.802, R2ext = 0.861) in terms of prediction accuracy. The SHAP analysis revealed that temperature, molecular three-dimensional structure, and atomic ionization capacity were the main factors affecting the degradation efficiency. This study provides a scientific basis for optimizing the operating conditions of the Fenton process and enhancing the degradation efficiency, which is of great significance in guiding the research and practice in the field of water treatment.

fenton oxidation process  /  pollutant degradation rate constant log K  /  organic pollutants  /  machine learning model  /  SHAP analysis
于林堂, 陈咚咚, 陶翠翠, 朱腾义. 基于可解释性机器学习的芬顿工艺降解有机污染物速率的研究. 中国环境科学, 2025 , 45 (8) : 4294 -4302 .
Lin-tang YU, Dong-dong CHEN, Cui-cui TAO, Teng-yi ZHU. Research on the degradation rate of organic pollutants in fenton process based on explainable machine learning[J]. China Environmental Science, 2025 , 45 (8) : 4294 -4302 .
  • 国家自然科学基金资助项目(42077331)
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  • 接收时间:2025-01-03
  • 首发时间:2026-10-08
  • 出版时间:2025-08-20
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  • 收稿日期:2025-01-03
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国家自然科学基金资助项目(42077331)
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    扬州大学环境科学与工程学院,江苏 扬州 225127

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species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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