Machine Learning-Optimized Advanced Oxidation for Enhanced Sludge Dewatering: EPS Mechanistic Insights and Predictive Modeling

IF 11.4 1区环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL

Water Research Pub Date : 2025-05-10 DOI:10.1016/j.watres.2025.123804

Zi-Chen Ling, Jing-Jing Wang, Shi-Jie Yuan, Bin Dong, Xiao-Hu Dai

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Abstract

The recalcitrant nature of extracellular polymeric substances (EPS) in sewage sludge severely limits dewatering efficiency. While advanced oxidation processes (AOPs) disrupt EPS matrices, their optimization remains challenging. This study integrates machine learning (ML) with AOPs to establish predictive frameworks for parameter optimization. A Bayesian-optimized XGBoost model (test R² = 0.87, based on a 70/30 train-test split) outperformed other algorithms in predicting optimal AOP configurations, while an AdaBoost-based model (test R² = 0.81) provided mechanistic insights. Radical donor and catalyst concentrations exhibited synergistic effects (r > 0.8) in hydroxyl radical generation, with pH and VS/TS ratio critically influencing EPS dynamics. Mobile soluble EPS (S-EPS) dominated dewaterability control, whereas tightly bound EPS showed negligible impact. SHAP analysis identified radical donor dosage, catalyst loading, and pH as pivotal operational parameters, with acidic conditions enhancing EPS disruption. This work advances data-driven AOP optimization for sludge management, highlighting the need for dynamic EPS transformation studies and adaptive control systems to achieve sustainable wastewater treatment.

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机器学习优化污泥脱水的高级氧化：EPS机制见解和预测建模

污水污泥中胞外聚合物（EPS）的顽固性严重限制了脱水效率。虽然高级氧化过程（AOPs）会破坏EPS矩阵，但其优化仍然具有挑战性。本研究将机器学习（ML）与aop相结合，建立参数优化的预测框架。基于贝叶斯优化的XGBoost模型（测试R² = 0.87，基于70/30的训练-测试分割）在预测最佳AOP配置方面优于其他算法，而基于adaboost的模型（测试R² = 0.81）提供了机制见解。自由基供体浓度和催化剂浓度表现出协同效应(r >；pH和VS/TS比对EPS动力学有重要影响。移动可溶性EPS （S-EPS）对脱水性的影响较小，而紧密结合EPS对脱水性的影响较小。SHAP分析发现，自由基供体剂量、催化剂负载和pH值是关键的操作参数，酸性条件会增强EPS的破坏。这项工作推进了数据驱动的污泥管理AOP优化，强调了动态EPS转换研究和自适应控制系统的需求，以实现可持续的废水处理。

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来源期刊

Water Research 环境科学-工程：环境

CiteScore

20.80

自引率

9.40%

发文量

1307

审稿时长

38 days

期刊介绍： Water Research, along with its open access companion journal Water Research X, serves as a platform for publishing original research papers covering various aspects of the science and technology related to the anthropogenic water cycle, water quality, and its management worldwide. The audience targeted by the journal comprises biologists, chemical engineers, chemists, civil engineers, environmental engineers, limnologists, and microbiologists. The scope of the journal include: •Treatment processes for water and wastewaters (municipal, agricultural, industrial, and on-site treatment), including resource recovery and residuals management; •Urban hydrology including sewer systems, stormwater management, and green infrastructure; •Drinking water treatment and distribution; •Potable and non-potable water reuse; •Sanitation, public health, and risk assessment; •Anaerobic digestion, solid and hazardous waste management, including source characterization and the effects and control of leachates and gaseous emissions; •Contaminants (chemical, microbial, anthropogenic particles such as nanoparticles or microplastics) and related water quality sensing, monitoring, fate, and assessment; •Anthropogenic impacts on inland, tidal, coastal and urban waters, focusing on surface and ground waters, and point and non-point sources of pollution; •Environmental restoration, linked to surface water, groundwater and groundwater remediation; •Analysis of the interfaces between sediments and water, and between water and atmosphere, focusing specifically on anthropogenic impacts; •Mathematical modelling, systems analysis, machine learning, and beneficial use of big data related to the anthropogenic water cycle; •Socio-economic, policy, and regulations studies.