Advances in tailings thickening: A rheology-oriented review of theory and practice

Tailings thickening is a key unit operation in mineral processing, paste backfilling, and tailings management systems, exerting a direct influence on water recovery efficiency, slurry transport behavior, and the stability of downstream dewatering and disposal processes. Throughout the entire thickening workflow, spanning free settling, compression settling, and high-concentration discharge, the evolution of rheological properties governs the formation of particle networks, the development of yield stress, and the resistance to flow and consolidation. These rheological responses are strongly coupled with particle size distribution, mineral composition, surface physicochemical characteristics, and operating conditions, leading to complex, stage-dependent thickening behavior. This review adopts a rheology-oriented perspective to analyze the thickening process across its distinct zones—clarification, free settling, hindered settling, and compression. Rheological behavior evolves from Newtonian to non-Newtonian and viscoplastic as solid concentration increases, affecting particle aggregation, settling, and network consolidation. Key parameters—yield stress, viscosity, and viscoelastic moduli—are central to understanding flocculation (e.g., DLVO, adsorption-bridging), settling, and compression. Integrating rheology with macroscopic models (e.g., C-C, Kynch, B-W) and microscopic theories provides a unified framework for interpreting thickening mechanisms and guiding optimization. Advances in rheometry and online monitoring enable accurate slurry characterization, while numerical simulations incorporating rheology support the prediction of flow fields and solid-liquid separation. This review underscores the necessity of a rheology-based approach for designing flocculant dosing, optimizing equipment, and diagnosing failures. Future thickening technologies will rely on rheology combined with multi-scale modeling, intelligent monitoring, and AI-based control for real-time regulation and sustainable tailings management.