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

Authors

Xing Zhang, Yu Huang, Yafei Shi, Wei Zhang, Yuantao Lin, Jinkai Yan, Youzhou Jiao, Yalin Li

Published in

Waste management (New York, N.Y.). Volume 226. Pages 115814. Aug 15, 2026. Epub Aug 15, 2026.

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.

PMID:
42603434
Bibliographic data and abstract were imported from PubMed on 16 Aug 2026.

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