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Machine-learning modeling and optimization of electro-osmotic dewatering of wastewater sludge: Current status and challenges.

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Title: Machine-learning modeling and optimization of electro-osmotic dewatering of wastewater sludge: Current status and challenges.
Authors: Zhang X; School of Environment and Biological Engineering, Henan University of Engineering, Zhengzhou 451191, PR China; School of Civil Engineering, Architecture and Environment, Hubei University of Technology, Wuhan 430068, PR China., Huang Y; School of Civil Engineering, Architecture and Environment, Hubei University of Technology, Wuhan 430068, PR China., Shi Y; School of Civil Engineering, Architecture and Environment, Hubei University of Technology, Wuhan 430068, PR China., Zhang W; School of Ecology and Environment, Zhengzhou University, Zhengzhou 450001, PR China., Lin Y; College of Intelligent Robotics and Advanced Manufacturing, Fudan University, Shanghai 200433, PR China., Yan J; School of Environmental Engineering, Henan University of Technology, Zhengzhou 450001, PR China., Jiao Y; College of Resources and Environment, Henan Agricultural University, Zhengzhou 450002, PR China., Li Y; School of Environment and Biological Engineering, Henan University of Engineering, Zhengzhou 451191, PR China; School of Civil Engineering, Architecture and Environment, Hubei University of Technology, Wuhan 430068, PR China. Electronic address: li_ya_lin@haue.edu.cn.
Source: Waste management (New York, N.Y.) [Waste Manag] 2026 Oct 30; Vol. 226, pp. 115814. Date of Electronic Publication: 2026 Aug 15.
Publication Type: Journal Article; Review
Language: English
Journal Info: Publisher: Pergamon Press Country of Publication: United States NLM ID: 9884362 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-2456 (Electronic) Linking ISSN: 0956053X NLM ISO Abbreviation: Waste Manag Subsets: MEDLINE
Imprint Name(s): Original Publication: New York : Pergamon Press, c1989-
MeSH Terms: Sewage*/chemistry , Waste Disposal, Fluid*/methods , Wastewater*/chemistry , Electroosmosis*/methods , Machine Learning*, Soft Computing ; Predictive Learning Models ; Osmosis
Abstract: Electro-osmotic dewatering (EOD) is a promising technology for enhanced sludge dewatering and volume reduction. However, its engineering application is constrained by multiphysics coupling, partially observed internal states, sludge variability, and trade-offs among dewatering efficiency, energy consumption, treatment time, and electrode stability. Machine learning (ML) offers opportunities to represent nonlinear process behavior, estimate difficult-to-measure states, and support optimization and control. Nevertheless, existing studies remain fragmented and lack standardized feature definitions and data-reporting practices, task-oriented workflows, cross-condition validation, and consistent mechanistic interpretation. This review links EOD mechanisms and process-variable evolution to specific ML requirements and organizes applications into four categories, namely point prediction, time-series forecasting, visual soft sensing, and multi-objective optimization. The suitability of different ML methods is critically examined, with emphasis on hybrid and physics-informed modeling, interpretability, uncertainty evaluation, and generalization under limited-data conditions. Four interrelated priorities are identified for developing reliable and deployable ML-enabled EOD systems. The first is to standardize features, metadata, and benchmark evaluation, and the second is to integrate data-driven models with physical constraints. The third is to strengthen external validation, model transferability, and uncertainty quantification, and the fourth is to advance toward intelligent closed-loop EOD systems. These priorities can be implemented through short-, medium-, and long-term stages, progressing from reproducible data foundations through transferable models to adaptive engineering systems. By distinguishing direct EOD evidence from transferable methodological examples, this review provides a task-oriented and deployment-aware roadmap for credible ML-enabled EOD research.
(Copyright © 2026 Elsevier Ltd. All rights reserved.)
Competing Interests: 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.
Contributed Indexing: Keywords: Data-driven modeling; Electro-osmotic; Machine learning; Multi-field coupling; Sludge dewatering
Substance Nomenclature: 0 (Sewage)
0 (Wastewater)
Entry Date(s): Date Created: 20260815 Date Completed: 20260917 Latest Revision: 20260917
Update Code: 20260918
DOI: 10.1016/j.wasman.2026.115814
PMID: 42603434
Database: MEDLINE
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  Data: Machine-learning modeling and optimization of electro-osmotic dewatering of wastewater sludge: Current status and challenges.
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  Data: <searchLink fieldCode="AU" term="%22Zhang+X%22">Zhang X</searchLink>; School of Environment and Biological Engineering, Henan University of Engineering, Zhengzhou 451191, PR China; School of Civil Engineering, Architecture and Environment, Hubei University of Technology, Wuhan 430068, PR China.<br /><searchLink fieldCode="AU" term="%22Huang+Y%22">Huang Y</searchLink>; School of Civil Engineering, Architecture and Environment, Hubei University of Technology, Wuhan 430068, PR China.<br /><searchLink fieldCode="AU" term="%22Shi+Y%22">Shi Y</searchLink>; School of Civil Engineering, Architecture and Environment, Hubei University of Technology, Wuhan 430068, PR China.<br /><searchLink fieldCode="AU" term="%22Zhang+W%22">Zhang W</searchLink>; School of Ecology and Environment, Zhengzhou University, Zhengzhou 450001, PR China.<br /><searchLink fieldCode="AU" term="%22Lin+Y%22">Lin Y</searchLink>; College of Intelligent Robotics and Advanced Manufacturing, Fudan University, Shanghai 200433, PR China.<br /><searchLink fieldCode="AU" term="%22Yan+J%22">Yan J</searchLink>; School of Environmental Engineering, Henan University of Technology, Zhengzhou 450001, PR China.<br /><searchLink fieldCode="AU" term="%22Jiao+Y%22">Jiao Y</searchLink>; College of Resources and Environment, Henan Agricultural University, Zhengzhou 450002, PR China.<br /><searchLink fieldCode="AU" term="%22Li+Y%22">Li Y</searchLink>; School of Environment and Biological Engineering, Henan University of Engineering, Zhengzhou 451191, PR China; School of Civil Engineering, Architecture and Environment, Hubei University of Technology, Wuhan 430068, PR China. Electronic address: li_ya_lin@haue.edu.cn.
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  Data: <searchLink fieldCode="JN" term="%229884362%22">Waste management (New York, N.Y.)</searchLink> [Waste Manag] 2026 Oct 30; Vol. 226, pp. 115814. <i>Date of Electronic Publication: </i>2026 Aug 15.
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  Data: Journal Article; Review
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  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Pergamon+Press%22">Pergamon Press </searchLink><i>Country of Publication: </i>United States <i>NLM ID: </i>9884362 <i>Publication Model: </i>Print-Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>1879-2456 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%220956053X%22">0956053X </searchLink><i>NLM ISO Abbreviation: </i>Waste Manag <i>Subsets: </i>MEDLINE
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  Data: <i>Original Publication</i>: New York : Pergamon Press, c1989-
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  Data: <searchLink fieldCode="MM" term="%22Sewage%22">Sewage*</searchLink>/<searchLink fieldCode="MM" term="%22Sewage+chemistry%22">chemistry</searchLink> <br /><searchLink fieldCode="MM" term="%22Waste+Disposal%2C+Fluid%22">Waste Disposal, Fluid*</searchLink>/<searchLink fieldCode="MM" term="%22Waste+Disposal%2C+Fluid+methods%22">methods</searchLink> <br /><searchLink fieldCode="MM" term="%22Wastewater%22">Wastewater*</searchLink>/<searchLink fieldCode="MM" term="%22Wastewater+chemistry%22">chemistry</searchLink> <br /><searchLink fieldCode="MM" term="%22Electroosmosis%22">Electroosmosis*</searchLink>/<searchLink fieldCode="MM" term="%22Electroosmosis+methods%22">methods</searchLink> <br /><searchLink fieldCode="MM" term="%22Machine+Learning%22">Machine Learning*</searchLink><br /><searchLink fieldCode="MH" term="%22Soft+Computing%22">Soft Computing</searchLink> ; <searchLink fieldCode="MH" term="%22Predictive+Learning+Models%22">Predictive Learning Models</searchLink> ; <searchLink fieldCode="MH" term="%22Osmosis%22">Osmosis</searchLink>
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  Label: Abstract
  Group: Ab
  Data: Electro-osmotic dewatering (EOD) is a promising technology for enhanced sludge dewatering and volume reduction. However, its engineering application is constrained by multiphysics coupling, partially observed internal states, sludge variability, and trade-offs among dewatering efficiency, energy consumption, treatment time, and electrode stability. Machine learning (ML) offers opportunities to represent nonlinear process behavior, estimate difficult-to-measure states, and support optimization and control. Nevertheless, existing studies remain fragmented and lack standardized feature definitions and data-reporting practices, task-oriented workflows, cross-condition validation, and consistent mechanistic interpretation. This review links EOD mechanisms and process-variable evolution to specific ML requirements and organizes applications into four categories, namely point prediction, time-series forecasting, visual soft sensing, and multi-objective optimization. The suitability of different ML methods is critically examined, with emphasis on hybrid and physics-informed modeling, interpretability, uncertainty evaluation, and generalization under limited-data conditions. Four interrelated priorities are identified for developing reliable and deployable ML-enabled EOD systems. The first is to standardize features, metadata, and benchmark evaluation, and the second is to integrate data-driven models with physical constraints. The third is to strengthen external validation, model transferability, and uncertainty quantification, and the fourth is to advance toward intelligent closed-loop EOD systems. These priorities can be implemented through short-, medium-, and long-term stages, progressing from reproducible data foundations through transferable models to adaptive engineering systems. By distinguishing direct EOD evidence from transferable methodological examples, this review provides a task-oriented and deployment-aware roadmap for credible ML-enabled EOD research.<br /> (Copyright © 2026 Elsevier Ltd. All rights reserved.)
– Name: Abstract
  Label: Competing Interests
  Group: Ab
  Data: 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.
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  Data: <i>Keywords: </i>Data-driven modeling; Electro-osmotic; Machine learning; Multi-field coupling; Sludge dewatering
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  Data: 0 (Sewage)<br />0 (Wastewater)
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  Data: <i>Date Created: </i>20260815 <i>Date Completed: </i>20260917 <i>Latest Revision: </i>20260917
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