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Froth flotation is a widely used method for the concentration of sulfide minerals. The structure of the superficial froth is an indicator of the performance of froth flotation alongside with the operational conditions in which this process is carried out. The aim of this study is to explore how the different operational conditions that can be managed in a flotation plant could directly influence the observable characteristics of the superficial froth. For this purpose, a froth image database was created using a special laboratory cell, designed to emulate the conditions seen in an industrial flotation cell. The database contains 2250 images, distributed in 45 categories; each category has a specific combination of testing conditions, and the main visual characteristics are observed. It also includes a methodology used to assess the quality of each corresponding category. Dataset: doi.org/10.6084/m9.figshare.21437292 Dataset License: CC BY 4.0 [ABSTRACT FROM AUTHOR]

Comminution is the stage with the largest energy consumption in the mining process. Therefore, several pre-treatments have been proposed to reduce the energy requirements of this stage. This work analyzed the effect of a heating pre-treatment on the Bond index. A conventional heating pre-treatment was applied to a Chilean copper ore. The ore was heated to temperatures from 300 to 600 °C using a conventional furnace, resulting in a reduction of 19% in the Bond work index. Due to the pre-treatment, the mineral cracked in several areas. Microfracture and composition analyses of these areas confirmed that crack generation in the ore is due to the thermal stress produced by the pre-treatment. The fracture analysis explains the reduction in the Bond work index, since crack generation started at similar temperatures to those at which the reduction in the Bond work index was observed. In addition, the analysis also shows that micro-cracks occur between and through different phases, which may have an impact on mineral liberation. These results also show that, under a moderate high temperature, an important reduction in energy consumption can be obtained. [ABSTRACT FROM AUTHOR]

The effects of halophilic bacteria (Halobacillus sp. and Marinobacter sp.) on pyrite and chalcopyrite surface oxidation in artificial seawater are studied by electrochemical impedance spectroscopy (EIS) in conjunction with X-ray diffraction (XRD) and cyclic voltammetry analysis (CV), in order to explain the influence of these microorganisms on the minerals floatability. EIS analyses on pyrite electrodes suggest that biomaterial from both bacteria adheres to the mineral surface, which is reinforced by CV experiments as capacitive currents are promoted by both bacteria. Additionally, XRD analyses of pyrite samples after immersion in artificial seawater with and without bacteria indicate the formation of hematite on the mineral surface in the presence of Halobacillus sp., which together with the adherence of biomaterial could promote the depression of pyrite during flotation. On the other hand, EIS and CV analyses on chalcopyrite electrodes suggest that the adherence of Halobacillus sp. and Marinobacter sp. to the surface of the mineral have no significant effects on the kinetics of the chalcopyrite oxidation processes. These results together with XRD analyses of the chalcopyrite samples after immersion in artificial seawater with and without bacteria suggest that superficial sulphur might have a stronger influence on chalcopyrite flotability than the presence of bacteria. [ABSTRACT FROM AUTHOR]

Objective: This study aims to investigate the attachment of Acidithiobacillus ferrooxidans to pyrite in two different environments: fresh and saline water (water with 35 g/L of NaCl or 0.6 M). Adsorption isotherms were analyzed using the Langmuir and Freundlich models. Saline water is water with 35 g/L of NaCl (0.6 M), which is the concentration of NaCl in seawater. The use of raw seawater in mining is becoming relevant in leaching and flotation process. At the same time the use of microorganisms in both processes is gaining attention. For this reason, it is important to study the behavior of adherence of microorganisms to minerals in saline aqueous environments, similar to seawater. Results: The bacteria showed a higher level of attachment to pyrite in fresh water than in saline water. The Langmuir model fitted better the experimental data obtained in fresh water than in saline water with a coefficient of determination (R2) of 0.85 and 0.61 for fresh and saline water, respectively. Conclusions: This suggests that the bacteria tend to adhere more as a monolayer in fresh than in saline water in the early stage of adhesion. [ABSTRACT FROM AUTHOR]

Integration of renewable energy into mining and processing operations is becoming necessary as part of a strategy towards sustainability in the minerals industry. A solar photovoltaic plant along with a battery energy storage system (PV-BESS) can provide a long-term solution to cope with increasing energy costs, thus reducing the tension with other societal competing needs for a key resource such as clean energy. However, sizing these systems is challenging, in face of uncertain ore grindability and solar power availability. In this paper, we present an application of an integrated model to size the PV-BESS, where the variability of ore grindability is modeled using geostatistical tools, solar irradiance variability is captured using a Markov chain simulation model, and the entire system is optimized through linear stochastic optimization, considering a fixed mine schedule for the feed of a semiautogenous grinding (SAG) mill. The main goals are to minimize the costs associated with operating the SAG mill in the presence of a PV-BESS system, understand how the sizing and costs change under the influence of stochastic drivers, and reveal the potential for demand-side management in mines. The size and costs of the necessary infrastructure are determined for a model of estimated grindability and an ensemble of 50 simulated models of grindability, and compared against the sizing and cost of the ground truth. The effect of climate change on solar energy availability is accounted for by forecasting the ratio of excellent, good, and moderate days over bad days in terms of irradiance out to the year 2030. Finally, the effects of stockpiles and feed control according to the processing plant needs, namely a demand-side management approach, are evaluated to reveal the impact on the energy requirements and the sizing of the photovoltaic and storage system. The model is optimized considering a yearly cost function, with hourly resolution for the solar irradiance and hardness models. The results show that integrating solar power into the operation of a SAG mill has potential to reduce the total energy cost by 27%. Robustness against climate change can be achievedwith an increase in total cost of 1%. Finally, use of stockpiles to manage the ore supply to the mill and minimize the energy cost to process it results in a cost reduction of around 2%, which should offset the rehandling cost of managing the stockpiles. [ABSTRACT FROM AUTHOR]

This paper deals with the counting and measuring of overlapping circular objects in binary images, a problem that arises in the mineral processing industry when estimating the distribution of bubble diameters in flotation cells in order to predict the geometallurgical performance of the flotation process. By viewing the images as realizations of a stationary planar Boolean model with circular grains and by using two-point set statistics, it is possible to jointly estimate the average number of objects per unit area and their diameter distribution. When applied to real and simulated images, this method is shown to give accurate estimates, to be robust to the presence of noise contaminating the images and of moderate drifts in the number of objects per unit area, and to speed up the processing time with respect to currently used methods. Combined, these properties serve to make the method suitable for an on-line monitoring of the flotation process. [ABSTRACT FROM AUTHOR]

Acidithiobacillus ferrooxidans has been shown to be a good depressant of pyrite in freshwater and seawater flotation. However, the effect of these bacteria over copper-activated pyrite has not been studied. At the industrial scale, the activation of pyrite with copper is a common process that occurs because Cu 2 + ions, released from other minerals, react with pyrite. This is a problem because Cu 2 + ions facilitate the reaction of pyrite with the xanthate collectors, becoming hydrophobic and reaching the froth. In this study, microflotation experiments in a Hallimond tube were conducted to evaluate the depressant effect of A. ferrooxidans over non-activated and Cu-activated pyrite in freshwater and seawater flotation. The experiments were carried out at pH 4, 6, 8, 10 and 12 and pyrite was mixed with CuSO4 at 2.5 × 10 − 5 and 5 × 10 − 5 M in order to activate its surface. Considering the results obtained in the microflotation tests, it is possible to conclude that Acidithiobacillus ferrooxidans is able to depress non-activated and Cu-activated pyrite at the entire pH range studied (4–12) in freshwater. On the other hand, the use of bacteria in flotation with seawater proved to be effective to depress non-activated and Cu-activated pyrite at pH 8 and 10 with better results achieved at pH 10. At this pH, the non-activated pyrite recovery dropped from 96% to 15%, and the recovery of Cu-activated pyrite dropped from 95% to 32% when the activation was carried out at 2.5 × 10 − 5 M, and from 87% to 50% when the activation was conducted at 5 × 10 − 5 M of CuSO4. The XPS analysis showed that chalcopyrite and copper (II) hydroxide were formed on the pyrite surface when it is contacted with CuSO4. [ABSTRACT FROM AUTHOR]

Ore hardness plays a critical role in comminution circuits. Ore hardness is usually characterized at sample support in order to populate geometallurgical block models. However, the required attributes are not always available and suffer for lack of temporal resolution. We propose an operational relative-hardness definition and the use of real-time operational data to train a Long Short-Term Memory, a deep neural network architecture, to forecast the upcoming operational relative-hardness. We applied the proposed methodology on two SAG mill datasets, of one year period each. Results show accuracies above 80% on both SAG mills at a short upcoming period of times and around 1% of misclassifications between soft and hard characterization. The proposed application can be extended to any crushing and grinding equipment to forecast categorical attributes that are relevant to downstream processes. [ABSTRACT FROM AUTHOR]