• Machine learning-optimized adv...

*Result*: Machine learning-optimized advanced oxidation for enhanced sludge dewatering: EPS mechanistic insights and predictive modeling.

Title:
Machine learning-optimized advanced oxidation for enhanced sludge dewatering: EPS mechanistic insights and predictive modeling.
Authors:
Ling ZC; School of Environmental Science and Engineering, Tongji University, Shanghai 200092, China., Wang JJ; School of Environmental Science and Engineering, Tongji University, Shanghai 200092, China., Yuan SJ; School of Environmental Science and Engineering, Tongji University, Shanghai 200092, China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, China. Electronic address: shijie@mail.ustc.edu.cn., Dong B; School of Environmental Science and Engineering, Tongji University, Shanghai 200092, China; China Three Gorges Corporation, YANGTZE Eco-Environment Engineering Research Center, Beijing 100038, China., Dai XH; School of Environmental Science and Engineering, Tongji University, Shanghai 200092, China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, China.
Source:
Water research [Water Res] 2025 Sep 01; Vol. 283, pp. 123804. Date of Electronic Publication: 2025 May 10.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Pergamon Press Country of Publication: England NLM ID: 0105072 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-2448 (Electronic) Linking ISSN: 00431354 NLM ISO Abbreviation: Water Res Subsets: MEDLINE
Imprint Name(s):
Original Publication: Oxford, Pergamon Press.
MeSH Terms:
Waste Disposal, Fluid*/methods , Sewage*/chemistry , Machine Learning* , Predictive Learning Models*, Extracellular Polymeric Substance Matrix/metabolism ; Water Pollution/statistics & numerical data ; Models, Chemical ; Desiccation
Contributed Indexing:
Keywords: Advanced oxidation; Extracellular Polymeric Substances; Machine learning; Sludge dewatering
Substance Nomenclature:
0 (Sewage)
Entry Date(s):
Date Created: 20250515 Date Completed: 20250701 Latest Revision: 20250701
Update Code:
20260130
DOI:
10.1016/j.watres.2025.123804
PMID:
40373373
Database:
MEDLINE

*Further Information*

*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. Soluble EPS (S-EPS) dominated dewaterability control, whereas tightly bound EPS showed negligible impact. SHAP (SHapley Additive exPlanations) 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.
(Copyright © 2025 Elsevier Ltd. All rights reserved.)*

*Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.*