Abstract

Deploying new technologies in the mining industry is notoriously slow, particularly when these innovations target mineral systems with inherently complex or sluggish leaching behaviour. Primary copper sulfides are a prime example: they exhibit slow kinetics, therefore requiring long leach times. When testing new or established leaching approaches, inconsistent methodologies, inadequate mineralogical characterization, or deviations from standard procedures can yield ambiguous results that obscure true performance. Consequently, promising technologies may fail to advance simply because the underlying testwork is non-standardized or misaligned with the mineralogical characteristics of primary sulfide ores.

This paper addresses these limitations by presenting a rigorous, unbiased, and standardized testing framework jointly implemented by an independent laboratory (SGS Burnaby) and Jetti Resources. The case study revolves around Jetti’s catalytic leaching technology. The primary objective is to illustrate how a structured approach, beginning with sample selection, preparation and characterization, and continuing through statistically validated metallurgical testwork, enables reliable evaluation of emerging leach technologies. This approach reduces technical uncertainty and accelerates early-stage decision making.

To generate meaningful and reproducible results, test programs must deliberately integrate representative sample preparation, mineralogical diagnostics, sequential leach characterization, and testwork design across multiple particle sizes. The methodology presented here includes: (i) detailed mineralogical assessment; (ii) sequential leach and complementary characterization tests; and (iii) leach evaluations across stirred reactors (P80 ~76 µm), bottle rolls (≈2 mm), and column tests (>2 cm). At each stage, disciplined adherence to SOPs (Standard Operating Procedures), precise sample handling and rigorous analytical QA/QC are emphasized. Close communication between the laboratory and the client, along with continuous data validation, is also crucial. Clear decision points following characterization and stirred reactor testing ensure that only suitably qualified samples proceed to more resource-intensive column testing, thereby reducing unnecessary cost and time.

Case studies from various programs demonstrate the practical application and value of this workflow. Examples highlight how duplicate tests, statistical treatment of variability, and unbiased parallel testing generate high-confidence datasets that support robust technical and commercial evaluations. Importantly, the methodology ensures that performance differences between control and catalyzed conditions are statistically significant rather than artifacts of natural sample variability or procedural inconsistencies.