This paper presents a research overview, reconciling key and useful case study findings, towards uncovering major causes of gold refractoriness and maximising extraction performance of specific gold flotation and bio-oxidation products. Through systematic investigation of the ore mineralogical and gold deportment properties, leaching mechanisms, and kinetic behaviour and pulp rheology, it was observed that the predominant cause of the poor extraction efficacy of one bio-oxidised product is the presence of recalcitrant sulphate minerals (e.g., jarosite and gypsum) produced during the oxidation process. This was followed by carbonaceous matter and other gangue minerals such as muscovite, quartz, and rutile. The underpining leaching mechanism and kinetics coupled with the pulp rheology were influenced by the feed mineralogy/chemistry, time, agitation/shear rate, interfacial chemistry, pH modifier type, and mechano-chemical activation. For instance, surface exposure of otherwise unavailable gold particles by mechano-chemical activation enhanced the gold leaching rate and yield. This work reflect the remarkable impact of subtle deposit feature changes on extraction performance.

Froth flotation, a widely used mineral beneficiation technique, generates substantial volumes of data, offering the opportunity to extract valuable insights from these data for production line analysis. The quality of flotation data is critical to designing accurate prediction models and process optimisation. Unfortunately, industrial flotation data are often compromised by quality issues such as outliers that can produce misleading or erroneous analytical results. A general approach is to preprocess the data by replacing or imputing outliers with data values that have no connection with the real state of the process. However, this does not resolve the effect of outliers, especially those that deviate from normal trends. Outliers often occur across multiple variables, and their values may occur in normal observation ranges, making their detection challenging. An unresolved challenge in outlier detection is determining how far an observation must be to be considered an outlier. Existing methods rely on domain experts’ knowledge, which is difficult to apply when experts encounter large volumes of data with complex relationships. In this paper, we propose an approach to conduct outlier analysis on a flotation dataset and examine the efficacy of multiple machine learning (ML) algorithms—including k-Nearest Neighbour (kNN), Local Outlier Factor (LOF), and Isolation Forest (ISF)—in relation to the statistical $2\sigma$ rule for identifying outliers. We introduce the concept of “quasi-outliers” determined by the $2\sigma$ threshold as a benchmark for assessing the ML algorithms’ performance. The study also analyses the mutual coverage between quasi-outliers and outliers from the ML algorithms to identify the most effective outlier detection algorithm. We found that the outliers by kNN cover outliers of other methods. We use the experimental results to show that outliers affect model prediction accuracy, and excluding outliers from training data can reduce the average prediction errors.