Academic Journal
Machine-learning modeling and optimization of electro-osmotic dewatering of wastewater sludge: Current status and challenges.
| Τίτλος: | Machine-learning modeling and optimization of electro-osmotic dewatering of wastewater sludge: Current status and challenges. |
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| Συγγραφείς: | 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. |
| Πηγή: | Waste management (New York, N.Y.) [Waste Manag] 2026 Oct 30; Vol. 226, pp. 115814. Date of Electronic Publication: 2026 Aug 15. |
| Τύπος έκδοσης: | Journal Article; Review |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | 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- |
| Ιατρικοί όροι (MeSH): | Sewage*/chemistry , Waste Disposal, Fluid*/methods , Wastewater*/chemistry , Electroosmosis*/methods , Machine Learning*, Soft Computing ; Predictive Learning Models ; Osmosis |
| Περίληψη: | 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 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1879-2456 |
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| DOI: | 10.1016/j.wasman.2026.115814 |