Scale-Up in Mineral Processing: From Mechanism Preservation to Flowsheet-Scale Transferability—A Critical Review and the MINESCALE Framework
Keywords:
Mineral processing, Scale-up, Transferability, Process modelingAbstract
Scale-up in mineral processing is often reduced to equipment enlargement or empirical factors, although industrial performance emerges from coupled mechanisms whose relative importance changes with scale, continuity, inventory, recycle, ore variability, and control complexity. This structured critical narrative review synthesizes 144 contemporary sources published between 2020 and 2026 and draws on six foundational works to anchor canonical scale-up theory. The review covers comminution, classification, gravity and magnetic separation, flotation, solid–liquid separation, material handling, slurry transport, digital models, and integrated flowsheets. Evidence is interpreted through a mechanism-preservation perspective: laboratory performance is transferable only when the state variables controlling breakage, particle trajectories, hydrodynamics, residence-time distribution, rheology, interfacial phenomena, energy dissipation, segregation, and control dynamics are preserved, reproduced, or explicitly represented in an independently validated model. Ore variability is treated as the first scale-up boundary, followed by continuity and circuit integration. Quantitative cross-scale evidence shows that flotation batch-to-plant factors vary with particle size and liberation; classifier changes can alter throughput and specific grinding energy; and a continuous mini-pilot flowsheet at Kamoa-Kakula closely predicted industrial copper recovery after commissioning. We propose the MINESCALE framework, comprising eight decision gates: feed representativeness, dominant mechanism, similarity criteria, variability, pilot relevance, model validation, flowsheet integration, and industrial transferability. Retrospective stress testing against selected published cross-scale cases suggests that the framework can distinguish among successful experiments, externally validated equipment models, and more transferable integrated design bases. Scale-up is therefore better treated as an auditable evidence-transfer problem than as size-based extrapolation.
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