Mineral Processing Operations Research Articles

Machine learning for particle size prediction in iron ore grinding process

The mining industry is finally embracing the new opportunities brought by Industry 4.0, and one opportunity to be explored is the use of machine learning and artificial intelligence tools. To meet the continuous global demand for iron ore and improve the performance of facilities, process optimization is essential and may revolutionize the industry with data-based decision-making and increased efficiency and profitability. In iron ore beneficiation plants, the potential gains from machine learning tools are significant, and tend to increase in the milling process, where the grinding phase incurs the highest energy costs among mineral processing operations. This article presents the application of machine learning techniques for the prediction of the main product quality parameter of a milling plant. CRISP-DM, a well-established approach for data science projects, was applied, and the results demonstrated the effectiveness of the model, achieving satisfactory predictive performance. The deployment reduces reliance on laboratory testing and provides energy savings through proactive process control optimization.

A simplified rare earth element mining project cost estimator - a new tool for evaluating future mine supply

AbstractRare earth elements (REE) are essential for the future of electric vehicles and renewable energy production, but currently there is limited REE production outside of China. Forecasting global future supplies of REE often relies on the valuation of advanced mining projects to assess if projects will initiate production. Classical valuation methods, such as net present value (NPV), require knowledge of initial capital and operating expenditures to assess the economic feasibility of a given project. Typical industry unit expenditure projections are highly precise but are extremely project dependent and require detailed knowledge of a site that is far beyond the level that a domestic and/or global supply-demand model can obtain. To model future REE supplies we developed a new globally representative, simplified, mining project cost model that can be used to approximate the capital and operating expenditures of greenfield REE mining projects. Results from this model allow users to estimate expenses of open-pit and underground mines with mineral processing operations that range from solely beneficiation to individual REE separation. Using our new model and an estimate of NPV, we evaluate several REE mining projects and assess the economic variables impacting the likelihood for these projects to advance to the production stage. Of 12 assessed projects located outside of China, in the early to advanced development stages, nine had positive NPV at 2011 peak REE prices, but none had positive NPV when prices reached bottom in 2016. Two projects yielded positive NPV’s using 2022 and projected 2028 prices. The viability of these projects given the impact of the volatility in REE prices explains the high uncertainty in the future supply for these mineral commodities.

Sediment-Hosted Rare-Earth Elements Mineralization from the Dian-Qian District, Southwest China: Mineralogy and Mode of Occurrence

The Xuanwei Formation’s claystones in the Dian-Qian District of Southwest China are rich in rare-earth elements (REEs), suggesting their potential as a source of medium and heavy rare earths. However, the REE content in these rocks is lower than other types of rare-earth deposits, and the interrelationship among clay minerals is intricate. There is no direct evidence indicating the mineralization of REEs, limiting their beneficiation and extraction. The objective of this study is the characterization of REE distribution in the Dian-Qian District. The sedimentary rocks in this district are mainly composed of kaolinite, boehmite, quartz, rutile, and pyrite. The results of continuous chemical extraction of REE-rich claystone and transmission electron microscope (TEM) observations have confirmed that REEs occurred as florencite in the rocks, and that the ion-absorption state makes only a negligible contribution to the REE content. A close relationship between florencite and kaolinite makes traditional mineral processing operations very difficult. Combined with the properties of kaolinite, roasting-acid leaching was the efficacious approach for rare-earth resources extracted from the rare earth-rich clay rocks of the Xuanwei Formation.

Investigation of Seasonal Variations in Glencore’s Kidd Creek Cu-Zn ore flotation

Water management in mining and mineral processing operations is becoming increasingly important in the 21st century due to water scarcity concerns and as part of a greater push for sustainability. The aim of this study is to investigate the impacts of seasonal water quality in the Glencore’s Kidd Creek copper-zinc concentrator in Timmins, Ontario, Canada.Mineralogy results indicate that the Kidd ore consists primarily of pyrite (22%) and quartz (22%), followed by pyrrhotite (9%), sphalerite (8%) and chalcopyrite (7%).Investigating the flotation of Kidd Creek ore at bench scale between March 2022 and February 2023 identified seasonal trends in both copper and zinc flotation. However, post hoc analysis could not identify which months were significant. Oxidation of the ore samples or the use of process water could have contributed to an increase in sphalerite floated to the copper concentrate by 20 to 30% in summer months. Sphalerite surfaces in July have higher intensities of copper and collector species resulting in higher zinc recovery in the copper concentrate. Species in the process water and on the mineral surfaces may partially explain the differences in performance between the spring and the summer months.

Optimizing multi-spectral ore sorting incorporating wavelength selection utilizing neighborhood component analysis for effective arsenic mineral detection

Arsenic contamination not only complicates mineral processing but also poses environmental and health risks. To address these challenges, this research investigates the feasibility of utilizing Hyperspectral imaging combined with machine learning techniques for the identification of arsenic-containing minerals in copper ore samples, with a focus on practical application in sorting and processing operations. Through experimentation with various copper sulfide ores, Neighborhood Component Analysis (NCA) was employed to select essential wavelength bands from Hyperspectral data, subsequently used as inputs for machine learning algorithms to identify arsenic concentrations. Results demonstrate that by selecting a subset of informative bands using NCA, accurate mineral identification can be achieved with a significantly reduced the size of dataset, enabling efficient processing and analysis. Comparison with other wavelength selection methods highlights the superiority of NCA in optimizing classification accuracy. Specifically, the identification accuracy showed 91.9% or more when utilizing 8 or more bands selected by NCA and was comparable to hyperspectral data analysis with 204 bands. The findings suggest potential for cost-effective implementation of multispectral cameras in mineral processing operations. Future research directions include refining machine learning algorithms, exploring broader applications across diverse ore types, and integrating hyperspectral imaging with emerging sensor technologies for enhanced mineral processing capabilities.

Modeling and Simulation of Hydroxyapatite Recovery in the Desliming Circuit of the Tapira Industrial Plant, Brazil

The modeling and simulation of industrial mineral processing operations are traditionally used for cyclone sizing and optimizations of industrial operations. However, the main models used are based on the total population of particles in the pulp, thus not distinguishing the individual minerals. This article presents the results of an innovative method that investigated the optimization of the metallurgical recovery of P2O5 in the desliming circuit of a phosphate ore processing plant in Brazil. A survey campaign was carried out in the existing industrial circuit, followed by determining the partition curves for the overall particles and specifically for the hydroxyapatite particles. The results were used to calibrate the Narasimha–Mainza cyclone model. From a Base Case determined with reference to the industrial survey, three optimization scenarios were simulated through cyclone geometries and respective operating conditions changes. Simulated scenarios indicated the possibility of P2O5 metallurgical recovery increasing from 9.4% to 12.7% compared to the Base Case.

One-Class Classification-Based Monitoring of Mineral Processing Operations with Hyperensemble Tree-Models

Reliable monitoring of mineral process systems is key to more efficient plant operation. Multivariate statistical process control based on principal component analysis is well-established in industry, but may not be effective when dealing with nonlinear or transient processes, where process behaviour can change rapidly over time. Although many advances have been made in unsupervised process monitoring, the detection of incipient process drift or nascent abnormal process conditions remains an open research problem.In this investigation, monitoring of mineral process operations based on the use of random forest classifiers is considered. When the random forests are used, data representing normal operating conditions are split into subclasses with K-means clustering, after which an ensemble of random forests is trained as binary classifiers on the subclasses in a one-against-the-rest approach. These hyperensemble models are subsequently used to classify new operational data as in-control or out-of-control.One simulated and one real world case study are considered. In each case, the models were trained on data representing normal operating conditions and then tested on new process data that were generally different from the training data to test their ability to identify these data as out-of-control. In the real-world case study, the approach is compared with traditional multivariate statistical process monitoring based on a principal component model, as well as a recently proposed isolation forest model. The random forest-based models performed markedly better than the principal component model and slightly better than the isolation forest model as far as identification of out-of-control data were concerned, but this came at the expensive of a significantly larger false positive rate than these two models.

Identifying Separation Potency and Relationship of Grade and Density of Copper Ore for Gravity-Based Early Coarse Gangue Rejection using Statistical Method

The mineral processing industry faces a pressing challenge of declining productivity, particularly in metal minerals production, driven by soaring operational costs, with crushing and grinding operations accounting for a substantial portion of the energy consumption. To mitigate these challenges, early gangue rejection or preconcentration is proposed as an effective solution. This approach involves the removal of non-valuable materials and the concentration of valuable minerals at the initial processing stages, leading to increased efficiency, higher ore feed grades, cost savings, and reduced environmental impact. Various methods for gangue rejection exist, with gravity-based separation and ore sorting being the predominant options. Gravity-based separation stands out for its simplicity and versatility, making it the preferred choice in many scenarios. This study introduces a novel approach for evaluating gangue rejection potential, optimizing data distribution, and generating theoretical separation potential curves that account for both grade and density criteria. These curves provide insights into material rejection, metal losses, and optimal separation points, offering valuable guidance for mineral processing operations. Additionally, the study investigates the correlation between raw ore grade and density data, highlighting a strong relationship across different size fractions. This finding suggests that gravity-based separation can effectively rely on both grade and density parameters, resolving previous disparities observed in distribution models. By shedding light on advanced gangue rejection strategies, this research contributes to improved productivity and informed decision-making in the mineral processing industry.

Fire and Water: Geometallurgy and Extractive Metallurgy

There is no doubt that mineralogy, texture, and microfabric, as primary characteristics of an ore, affect mineral processing operations. Their direct effects on extractive metallurgical processes and the associated optimization potential, however, are less well documented. Here, we examine the status of geometallurgical approaches in extractive metallurgy by focusing on the effects of primary ore characteristics in hydrometallurgical and pyrometallurgical processes. Two selected case studies illustrate the linkages. Using quantitative data analytics on ores and concentrates, the possibilities for optimized and sustainable metal extraction and waste valorization are discussed.

Baseline assessment of naturally occurring radionuclides in borehole water of Asikam-gold mining community in Ghana

The main source of drinking water and domestic usage for the population of Asikam in the Fanteakwa South Municipality is from hand-dug wells and mechanized boreholes. This study assessed naturally occurring radionuclides Radium-226(Ra-226), Thorium-232(Th-232) and Patassium-40(K-40) in the water from these sources. A random sample of 20 sampling points made up of 7 manually dug wells and 13 mechanically drilled boreholes were taken from Asikam Communities. The radioactive contents of the selected water sources were characterized and analysed using gamma spectrometry system with High Pure Germanium Detector. This assessment was carried out to determine the level of natural radioactivity that the processing of gold ore at the Asikam illegal mining site in Ghana exposes the public to. The mean activity concentrations of Ra-226, Th-232, and K-40 were obtained as 0.30 Bq/L, 0.49 Bq/L, and 2.44 Bq/L in the water samples respectively. The results from this study were found to be lower than the Guidance levels recommended by the World Health Organization (WHO, 2011; IAEA,2014) of 1 Bq/l for Ra-226 and Th-232. The estimated average committed annual effective dose for water samples was 0.17 mSv/yr which is lower than the 1 mSv/yr recommended limits for public exposure by the WHO. The cancer fatality risk and hereditary consequences from exposure to naturally occurring radionuclides which may result from the Asikam community's mining and mineral processing operations were evaluated and found to be insignificant. The findings of this study demonstrate radioactivity levels are within the range of natural background radiation levels as reported in the literature, and they compare favorably with findings from comparable studies conducted nationally and internationally. This study provides important baseline information for future research in the study area.

Environmental impacts and improvement potentials for copper mining and mineral processing operations in China

There has been growing concern among the public over the environmental impacts of the copper (Cu) mining and mineral processing industries. As an effective tool enabling interactions of all energy and material flows with the environment, Life Cycle Assessment (LCA) is used in many countries to identify environmental hotspots associated with operations, based on which improvements can be made. However, robust LCA research in this sector is lacking in China. This study aimed to fill this critical gap by investigating two typical Cu mining and mineral processing operations using different mining technologies, based on globally harmonized LCA methodologies. The results of the overall environmental impacts were obtained using a sensitivity analysis. Electricity (38%–74%), diesel (8%–24%) and explosives (4%–22%) were identified as the three main controlling factors. At the same time, the mineral processing stage was found to be the major production stage (60%–79%), followed by the mining stage (17%–39%) and the wastewater treatment (1%–13%). Global Warming Potential (GWP) was prioritized as the most important environmental issue (59%) across the selected impact categories. In addition, it was initially found that underground mining technology has better environmental performance than open-pit technology. Finally, the potential for improvement was estimated and discussed for the three identified controlling factors. Using GWP as an example, using green electricity can effectively reduce CO2 emissions by 47%–67%, whereas replacing diesel and explosives with cleaner fuels and explosives may contribute to lower CO2 emissions by 6% and 9%, respectively.

102 Application of Quantitative Mineralogy to Determine Sources of Airborne Particles at a European Copper Smelter

Abstract To ensure that regulatory compliance is maintained at mineral processing operations, worker exposure is regularly monitored. Determining the quantity and type of airborne particles permits the operation to identify the sources of dust, implement proper dust suppressant strategies, and ultimately, limit worker exposure. Conventional methods of analysis, such as chemical assay, are unable to rigorously differentiate between phases containing the same elements and may result in ambiguity related to identifying the source of airborne dust. A combination of Quantitative Evaluation of Materials by Scanning Electron Microscope (QEMSCAN) and chemical characterization has been used to evaluate personally taken size selective aerosol samples at key locations throughout a Cu smelter in Europe. Surface samples were also examined using higher resolution measurements. The Cu phases present in the workplace air samples are reflective of the activities performed at specific locations. Near the anode and electric furnace, the majority of Cu in the airborne dust is carried in metals and oxide phases (60-70%), and in the batch preparation area where Cu concentrate is received, significant amounts of Cu are carried in sulfide minerals (chalcocite, chalcopyrite/bornite, >40%). The results of the analysis will aid in the identification of the origin of emissions specific to location and activity, assist personnel in developing the appropriate mitigation strategies to limit workers exposure, and further understanding of the health risks associated with exposure. Additional metals of concern (e.g, Pb, As, and Cd) are also considered throughout this program. Speaker Biography Michelle Kelvin is a geoscientist and environmental mineralogist. She specializes in characterizing dust emissions that originate from industrial operations using modern geochemical methods and X-ray beam technologies.

Reliability analysis of the compressed air supplying system in underground mines

Despite the high cost and low efficiency, compressed air is mostly used in underground mining for ore extraction, hoisting, and mineral processing operations. Failures of compressed air systems not only threaten the health and safety of workers but also contribute to inefficient control of airflow and stopped all equipment that operates by compressed air. In such uncertain conditions, mine managers are faced with the big challenge to supply enough compressed air, and therefore, the reliability evaluation of these systems is essential. This paper aims to analyze the reliability of the compressed air system using the Markov modeling approach as a case study, Qaleh-Zari Copper Mine, Iran. To achieve this, the state space diagram was constructed considering all relevant states for all compressors in the main compressor house of the mine. The failure and repair rate of all main and reserve compressors were calculated for all possible transitions between states to obtain the probability of being of the system in each of the states. Moreover, the probability of failure at any time period was considered to study the reliability behavior. The results of this study show that there is 31.5% probability that the compressed air supplying system is in operating condition with two main and one standby compressors. The system probability that two main compressors are remain in the operation without failure for one months is 92.32%. Furthermore, the lifetime of the system is estimated 33 months when at least one main compressor is active.

Nanofiltration for treatment of Western Australian bore water for mineral processing operations: A pilot scale study

Limited freshwater availability, increasing competition for water among multiple users, cost of purification, corporate sustainability goals, etc., are the main factors driving many industries to focus on water quality and usage. Increasing water reuse and accessing alternative sources of freshwater are potential strategies to improve water efficiency. This paper aims to identify water treatment options that produce suitable water for flotation and other mineral processing operations. It investigates the performance of nanofiltration membranes in producing suitable water quality for the separation of minerals based on the ionic strength tolerance threshold at which impurities begin to impact the separation process. In this study, the membranes used could reject >95 % of divalent cations (such as Ca2+ and Mg2+) and >74 % of monovalent ions (e.g., Na+ and Cl−) under the conditions of 12 bar of transmembrane pressure and 65 % of the recovery. These rejection rates were found to be sufficient to produce fit-for-purpose water for mineral processing. In addition, the effect of pressure on the mass transfer mechanism of each membrane was determined.

Circular Economy Models in Industry: Developing a Conceptual Framework

One of the trends in sustainable economic development is the transition to circular business models, which aim to optimize goods and material flows by taking into account their cyclical nature at all production stages. Adapting circular business models to mining and mineral processing operations can serve as an impetus for mining companies to upgrade and move to a new level of technological sophistication. This article attempts to develop a conceptual framework for circular business models factoring in the nature of production chains in coal mining, power generation, and metal production to reduce production costs, expand the product range, improve sustainable development indicators, and deliver growth in company value. This article is based on desk studies that were aimed at analyzing the efficiency of circular models, organizing data on experience in rational resource use in the mining industry, and transforming basic circular models into adapted ones for the environment in which coal companies operate. This study developed a conceptual framework of options for the implementation and evaluation of circular business models using an interdisciplinary approach and taking into account the specific features of coal companies and the principle of a resource being valuable throughout its whole life cycle. The proposed conceptual framework of circular business models can be used by Russian coal companies to design corporate strategies and long-term development plans.

Molecular Dynamics Simulations of the Interactions between a Hydrolyzed Polyacrylamide with the Face and Edge Surfaces of Molybdenite.

Process water used in mineral processing operations corresponds to water recovered from the thickeners and tailings dams, containing residual reagents such as hydrolyzed polyacrylamides (HPAMs). These polymers depress the flotation of different minerals, and their effect on molybdenite has been experimentally demonstrated. The objective of this work was to study the interactions between a segment of a HPAM with the face and edge of molybdenite. The sigma profile, the radial distribution functions of the HPAM, and the orientation and atomic density profiles of water molecules on the face and edge surfaces of molybdenite were calculated. The results obtained from molecular dynamics simulations showed that the interactions between the HPAM and molybdenite are mainly explained by the interactions of the amide group with the faces and edges of the mineral. Molecular dynamics simulations also showed that the HPAM molecule rearranges in such a way that the amide group moves towards the molybdenite face or edge, and the carboxylate group moves away from the mineral surface. The results obtained in the simulations showed that the interactions of the HPAM with the molybdenite edge are slightly stronger than the interaction of this molecule with the mineral face. Simulations demonstrated that the presence of the sodium and hydroxide ions reduces the concentration of HPAM around the face and edge surfaces, which is expected to affect HPAM adsorption on molybdenite. The conclusions obtained through molecular dynamics simulations are in line with the results obtained in previous studies carried out at a macroscopic scale, which reported that HPAMs adsorb onto molybdenite particles and reduce their hydrophobicity.

An efficient method for computational flow-based simulation of heat transfer in a rotary kiln with pilot scale validation

• Counter-current rotary kiln 5.27 m long, operated with two different feeds of gravel. • Temperature measurements taken along the kiln under various operating conditions. • Novel fast efficient computational model developed to predict bed temperature. • Predicted temperature profile in reasonable agreement with the measurements. • Model allows rapid, but reliable assessment of kiln design and operating conditions. Rotary kilns are used to carry out heating and chemical transformation in several mineral processing operations. A series of experimental runs was carried out using the realistic but controlled arrangement of a pilot rotary kiln to obtain temperature data for the validation of a novel model that couples a computational fluid dynamics model of the flame to a granular bed model through heat transfer terms. The rotary kiln was 5.27 m long, with an internal diameter of 410 mm, and two different feeds of inert standard commercial gravel were used in the runs, with the kiln operating in counter-current operation. Each of the feeds was run through the kiln under various operating conditions (feed rate, kiln rotation speed, fuel rate and weir height), and kiln temperature profiles were measured during each run. The effect of various operating parameters on heat transfer within the kiln was then determined. A fast and efficient computational model of the pilot kiln was developed to predict the burner flame and heat transfer in the kiln; the granular bed was modelled using a one-dimensional model which was coupled in a pragmatic way to the computational fluid dynamics gas phase model. Dependence of the predicted temperature profile on operating parameters is in reasonable agreement with the measurements, and the model can be run rapidly for efficient assessment of a large number of design and operating condition options. While there have been an increasing number of papers published in recent years reporting very detailed models of the motion in granular beds in rotary drums that are extremely valuable for a fundamental understanding of issues such as segregation, such models are very computationally intensive, and are still not feasible for industrial-scale kilns in which the particles are small. This paper indicates that good predictions of process parameters such as bed temperature are possible without such very time-consuming computations. Moreover, the experimental temperature measurements will be useful for future rotary kiln studies.

Process simulation to determine blending and residence time distribution in mineral processing plants

Mineral processing plant performance depends on multiple factors, including the feed and the parameters to control the process. In this work, we show how to assess plant performance using geometallurgical modeling and dynamic simulation. Several models that describe comminution, classification, flotation, and residence time distribution (RTD) are implemented as modules and then connected to represent generic plant configurations. The estimation of the RTD is used to assess the ore blending generated within the plant through a methodology based on weighting the ore contribution at the plant discharge. Additionally, the RTD is used to display the ore permanence at different plant stages, which can be used as an operational input to anticipate the consequences of a perturbation in the feed. Different simulation scenarios are tested using synthetic data, including different plant configurations, time support for blending assessment, and ore feeding sequence. The results show that the simulation is sensitive to these attributes. Significant differences are detected in the generated product compositions when the plant configuration is changed. Also, distinct mine plans can be evaluated through simulation, predicting their processing performance. Therefore, the simulation tool developed can be used to evaluate real mineral processing operations and to test different operative strategies.

A review of the influence of blast fragmentation on downstream processing of metal ores

Rock fragmentation is one of the most commonly optimized blasting outcomes due to its significant influence on the efficiency of downstream operations. This paper reviews the influence of blast fragmentation on processing of metal ores. Two aspects of rock fragmentation that influence downstream mineral processing operations include the particle size of the run-of-mine ore and the degree of internal microfracturing in the fragments induced by blasting. The main influences identified include productivity of comminution equipment, energy consumption in comminution equipment and mineral recovery. An increase in blasting energy results in higher throughput and energy reduction in comminution circuits. Increase in blasting energy results in energy savings in the overall mining operations. However, when increasing the blasting energy, the environmental and safety impacts must be taken into consideration. Blast-induced microfractures increase the permeability of the fragments, therefore enhancing mineral exposure to leaching reagents and consequently may lead to higher mineral recoveries, particularly in heap and dump leaching operations which employ-one-stage comminution. However, the textural characteristics of the ore also influence the mineral recovery. Further research should focus on establishing the relationship between blast intensity and mineral leaching recovery for metal ores of different textural properties.

Characterization of Particulate Matter Generated at a Nickel Smelter Using Quantitative Mineralogy

Activities performed at mineral processing operations are capable of producing significant quantities of dust. To ensure that regulatory compliance is maintained throughout operation, dust levels are monitored by routine analysis of air filter samples. Determining the quantity and type of particulate matter present in dust allows for the operation to identify the sources of dust and where warranted, implement a dust suppressant strategy. Conventional methods of analysis, such as chemical assay, are unable to rigorously differentiate between phases containing the same elements and may result in ambiguity related to the identification of dust sources. By incorporating Quantitative Evaluation of Materials by Scanning Electron Microscope (QEMSCAN) into their routine monitoring programs, the Sudbury INO smelter has greatly improved characterization of the Ni and Co dust emissions generated at the operation. To determine specific activities responsible for emissions, bulk particulate matter was sampled from several key locations at the smelter that regularly produce dust. QEMSCAN was able to distinguish between critical subspecies of Ni and Co and mineralogical, and chemical signatures in the dust that are representative of locational activity were established. An apportionment of elements contributed by each source was calculated based on this information. The results of the characterization study assisted personnel in developing strategies to mitigate dust emissions that originate from the Smelter operations, and have general implications to occupational hygiene and environmental risk assessment.