Machine Learning in Hydrometallurgical Critical-Metal Recovery: A Critical Review and Evidence-Maturity Framework for Reliable Deployment
Keywords:
External validation, Extractive metallurgy, Model transferability, Physicochemical consistency, Source-wise validation, Uncertainty quantificationAbstract
Machine learning (ML) is increasingly used across mineral processing and extractive metallurgy to predict and optimize hydrometallurgical recovery of critical metals from ores, industrial residues, spent lithium-ion batteries, permanent magnets, and dilute aqueous streams. High internal accuracy, however, does not show that a model will transfer to a new feedstock, campaign, or operating window. This critical review shifts attention from headline performance to the quality of the evidence supporting model use. A total of 14 peer-reviewed studies spanning leaching, solvent extraction, adsorption, bioleaching, and secondary-resource recovery were assessed using a common evidence lens covering data provenance, validation design, external transfer testing, uncertainty, interpretability, experimental confirmation, and demonstration scale. A 20-point Evidence Maturity Score (EMS) was developed and applied to the corpus, placing each study on a five-level scale from proof of concept to plant-level evidence. Scores ranged from 4 to 14, with a median of 6.5. Algorithm comparison and model interpretation were common, but source-wise transfer testing appeared in only five studies; calibrated uncertainty was not reported; and no study demonstrated pilot- or plant-scale operation of its ML model. Recurring risks include source leakage in literature-derived datasets, heterogeneous target definitions pooled into one response variable, and feature attribution without chemical or kinetic consistency checks. The review argues that hydrometallurgical ML should be judged by transferability, physicochemical plausibility, uncertainty awareness, and evidence that model-guided decisions improve real experiments or operations. It concludes with a practical reporting checklist for more reliable and deployment-ready ML in mineral processing and extractive metallurgy.
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