Obtaining detailed and precise information from a classified refractory gold ore has been a long-standing challenge in mineral processing and process mineralogy. Although the concept of diagnostic leaching has been extensively addressed in the literature, very little information is available linking this approach with current advanced characterization techniques such as automated mineralogy. The present research study aims to characterize the flotation concentrate of refractory gold ore by combining diagnostic leaching and automated mineralogy to examine its processability. The diagnostic leaching process was applied stepwise at five stages, and the automated mineralogy was performed on different size fractions of the flotation concentrate. The chemical (X-ray fluorescence (XRF), atomic absorption spectroscopy (AAS), and inductively coupled plasma-optical emission spectroscopy (ICP-OES)) and mineralogical (X-ray diffraction (XRD)) analyses of the feed sample confirmed that the ore is a low-grade gold ore with 0.7 g/t Au. Initially, bottle roll tests were carried out to investigate leaching behavior, and the ore’s refractory nature, and gold recoveries of bottle roll tests in different sizes were below 40 wt%; it is classified as a high refractory gold ore as a result of direct cyanide leaching. Bulk sulfide flotation was applied to increase the gold content of the material. Automated mineralogy results demonstrated that most of the gold in the concentrate is present as an invisible gold form, and 63.7 wt% of gold was distributed in pyrite. Diagnostic leaching results showed 39.7 wt% of total gold was leachable using direct cyanide leaching, and around 33 wt% of undissolved gold was located in pyrite and arsenopyrite.

Collector selection is a critical step in flotation, as it has a direct impact on product quality, flotation recovery, and selectivity. Collectors can consist of different components, and their effectiveness can vary depending on the type of ore being processed. The general practice in both literature and in industry is to use a mixture of collectors rather than a single collector. However, the use of a collector mixture introduces several complex issues. It is challenging to determine the specific effects of each collector on different minerals, as well as to understand the synergistic effects of mixed collectors in flotation. This study presents a novel investigation focusing on the impact of blends of NAX, AEROPHINE® 3422, and AERO® MX 5149, in varying dosages and combinations, on the flotation performance of Kupferschiefer copper ore. Kinetics flotation tests were conducted using a mechanical flotation cell with various combinations and dosages of listed collectors. For this investigation, different predictive models such as machine-learning (ML) and conventional regression analyses were developed. For model construction, a database including the results of comprehensive experimental results was constructed. The best performing model was selected considering statistical performance indicators and their performance on unseen data. A sensitivity analysis was conducted on the model to justify contributions of collectors on the copper recovery and grade. The results showed that the ML-based models provide compatible results with the expert opinions and have higher statistical performance than conventional modelling tools. According to the experimental results and models’ findings, it has shown that AEROPHINE® 3422 (a blend of isopropyl ethyl thionocarbamate and dithiophosphinate) was the most influential collector for the copper recovery. In addition, two ternary graphs were generated from the modeled data to formulate mixtures for different grades and recovery priorities.