Threshold-driven reuse of mineral processing water: selective treatment and AI-ready monitoring for fit-for-purpose...

Flotation process-water reuse is increasingly necessary in mineral processing, yet recycled water is a chemically active process stream rather than a neutral freshwater substitute. This review reframes flotation water reuse as an AI-enabled fit-for-purpose management problem that links water-quality mechanisms, threshold evaluation, selective upgrading, and intelligent control. Evidence from sulfide, phosphate, and spodumene systems shows that accumulated ions, residual reagents, fine particles, dissolved organics, and microbial activity interact to affect flotation performance. Water-quality thresholds should be treated as process-specific decision bands, not universal numerical limits. The proposed AI architecture fuses online sensors, laboratory confirmation, ore information, reagent history, and recycle-time data for water-state recognition, soft-sensing prediction of difficult-to-measure contaminants, flotation-response forecasting, adaptive threshold correction, and treatment-routing decision support. Within this architecture, model selection should be governed by decision-time data availability, chronological validation, uncertainty quantification, and operational fallback. An offline proof-of-concept using a public industrial iron-ore flotation dataset demonstrates process-quality prediction and illustrative context-dependent threshold switching. Because the dataset contains no recycled-water chemistry or water-balance measurements, the exercise does not validate freshwater saving, economic benefit, treatment routing, or industrial closed-loop control. The proposed framework instead specifies the data, fail-safe mechanisms, validation metrics, and techno-economic analysis and life-cycle assessment requirements needed for future plant-scale evaluation.