It has only recently been discovered that naturally prevailing microorganisms have a notable role in flotation in addition to chemical process parameters and overall water quality. This study’s aim was to assess the prevailing microbial communities in relation to process chemistry in a zinc and copper mineral flotation plant. Due to the limitations of cultivation-based microbial methods that detect only a fraction of the total microbial diversity, DNA-based methods were utilised. However, it was discovered that the DNA extraction methods need to be improved for these environments with high mineral particle content. Microbial communities and metabolism were studied with quantitative PCR and amplicon sequencing of bacterial, archaeal and fungal marker genes and shotgun sequencing. Bacteria dominated the microbial communities, but in addition, both archaea and fungi were present. The predominant bacterial metabolism included versatile sulfur compound oxidation. Putative Thiovirga sp. dominated in the zinc plant and the water circuit samples, whereas Thiobacillus spp. dominated the copper plant. Halothiobacillus spp. were also an apparent part of the community in all samples. Nitrogen metabolism was more related to assimilatory than dissimilatory nitrate and nitrite oxidation/reduction reactions. Abundance of heavy metal resistance genes emphasized the adaptation and competitive edge of the core microbiome in these extreme conditions compared to microorganisms freshly entering the process.

To save freshwater resources and comply with environmental regulations, minerals processing operations are transitioning to partially or fully closed water circulation. However, the accumulation of electrolytes and the addition of reagents lead to changes in water composition and may compromise flotation performance and plant maintenance. As a consequence, costly modifications are often required to cope with these challenges. Therefore, knowledge about water quality variation owing to closed water circulation and its potential effect on the flotation performance is crucial. The experimental methodology presented in this paper targeted three main objectives: (1) predicting the tendency of the accumulation of elements and compounds into the process water during comminution, flotation, and storage in tailings facilities; (2) establishing a relationship between laboratory results and plant historical water quality data; and (3) predicting the potential effect of recycling water on flotation performance. The results obtained with Boliden Kevitsa ore showed a good correlation between the water matrix of the actual process water on-site and that obtained in the ore dissolution tests done in the laboratory. The final water composition came close to the process water in terms of major elements and some of the minor elements. Additionally, the work presented in this paper demonstrated that a dissolution loop allowed us to predict the potential impact of the recycling water on the ore flotability. This methodology could serve as an aid for predicting water quality matrix variation and designing closed water circulation systems at existing and new plants.

The changes in water quality owing to recirculation of water in mineral processing plants can compromise the plant performance as well as maintenance needs. Therefore, mining process water quality assessment is becoming critical. Nevertheless, very few studies have investigated the suitability of the current analysis methodology practiced in certified laboratories for evaluating mining process water quality. This article presents two case studies to highlight the major issues encountered when performing sampling for physicochemical and chemical parameters in process water at two European mine sites using procedures from two certified laboratories. In addition, microorganisms were shown to be abundant in process waters and likely affect the mining water chemistries. However, the protocols used for microbial studies are not optimal for mining process samples, and need to be improved. The results showed difficulties in providing satisfactory results when analyzing control samples. Additionally, the analysis results presented a strong imbalance in TDS and sulfur compounds. Several potential causes associated with the poor quality of the analysis results have been outlined with a specific focus on the preservation methods. A literature review on the degradation of thiosalts suggested that the current preservation procedures are not suitable for preserving sulfur compounds. Moreover, the results indicated that the water matrix strongly influenced the validity of the chosen analysis method. In conclusion, the analysis methods should be customized for the different mining water matrix types in order to ensure the accuracy and reproducibility of the results.

Due to the rising pressure from governmental regulations, the water recycling rate has increased significantly in mining operations over the last decades, resulting in a high variation of process water quality, which could potentially impact the plant performance. The current effort to assess water quality in mining is shifting from managing water to fulfill environmental regulations (focus on the effluents) to controlling water quality to maintain the operating performance (focus on the water within the process). However, minimal effort has been made to design a dedicated sampling procedure for process water. This study investigates the use of multivariate variography and principal component analysis (PCA) for improving the process water sampling procedure at the Kevitsa Mine, Finland. The aim is to design a sampling procedure for evaluating water quality using two different types of datasets and illustrating the impact of the dataset structure on the sampling design. The results showed that the common spot sampling procedure generated a very high sampling error and was not the best practice for process water. The weekly sampling frequency used at the mine site, suitable for fulfilling environmental regulations was too low to capture the process water variation. Therefore, it is not recommended to use environmental water datasets for operating control purposes. The multivariate variographic analysis revealed the hidden cyclic variation through its ability to summarize the time variations and the correlation between multiple variables that were not visible through the classical univariate variogram approach. However, the number of increments recommended by the global multivariogram became impractically high. Hence, an alternative approach combining PCA to the mutivariogram was used to filter noise from the data and keep the relevant information. This study highlights the benefits of using multivariate variography to improve water sampling procedures in the mining industry and to reduce both operational and environmental risks associated with water quality variability. Thus, this method has the potential to be used in worldwide mining operations as a standard procedure for sampling water to provide reliable results.