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

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.