Mineral Processing Applications Research Articles

Regulating Microstructure and Macroscopic Properties in Saturated Salt Solutions Containing Disordered Anions and Cations by Magnetic Field.

Saturated aqueous salt solutions have diverse applications in food production, mineral processing, pharmaceuticals, and environmental monitoring. However, the random and disordered arrangement of ions in these solutions poses limitations across different fields. In this study, we employ magnetic fields to regulate the disordered arrangement by a comprehensive methodology combining contact angle measurement, Raman spectroscopy, X-ray diffraction, and molecular dynamics simulations on saturated KCl solutions. Our findings reveal that weak magnetic fields impede the formation of K-Cl contact pairs and disrupt hydrogen bond networks, particularly DDAA and free OH types. However, they facilitate the interaction between water molecules and ions, leading to an increase in the number of K-O and Cl-H contact pairs, along with an expansion in ion hydration radius. These changes affect macroscopic properties, including the interaction with solid substrates and potential solubility increases. Our experimental and simulation results mutually validate each other, contributing to a theoretical framework for studying magnetic field-material interactions.

Characterization of Magnetic and Mineralogical Properties of Slag Particles from WEEE Processing

Magnetic separation has wide-ranging applications in both mineral processing and recycling industries. Nevertheless, its conventional utilization often overlooks the interplay between mineral and particle characteristics and their impact on operational conditions, ultimately influencing the efficacy of the separation process. This work describes a methodology able to achieve the comprehensive characterization and classification of Waste Electrical and Electronic Equipment (WEEE) slag. The primary objective is to establish a meaningful connection between the distinct properties of slag phases and their influence on the separation process. Our methodology consists of several stages. Firstly, the WEEE slag is sieved into distinct size classes, followed by classification into magnetic susceptibility classes by using the Frantz Isodynamic separator. To quantify the magnetic susceptibility of each class, we used a magnetic susceptibility balance, and to identify paramagnetic and ferromagnetic fractions and phases within these magnetic susceptibility classes, we conducted vibrating-sample magnetometer measurements. Finally, to establish a meaningful link between the magnetic characterization, mineralogical, and particle-level details, Mineral Liberation Analysis was conducted for each magnetic susceptibility class. This in-depth analysis, encompassing both particle properties and magnetic susceptibility classes, provides a better understanding of the separation behavior of different phases and can help to enrich phases with a specific range of magnetic susceptibility values. This knowledge advances progress towards the development of predictive separation models that are capable of bridging the gap between theoretical understanding and practical application in the field of magnetic separation.

Modeling and simulation of a gold concentrator plant implementing a dissolution loop method

Mineral processing applications increasingly use recycled water to preserve freshwater natural resources and comply with environmental regulations. However, accumulating anions, cations, and reagents in the process water may affect plant flotation performance and production continuity. Therefore, many cost actions may be needed to mitigate the recycled water effects. Typically, the process water properties and their effects on flotation performance are unknown for a greenfield project. Often, the result is an over-scaling up of the process plant with an additional financial cost. The experimental methodology in the paper focuses on creating water for testing that is closer to the actual process water during the comminution and flotation process for any greenfield project. The scope of the study consists of creating possible process water, conducting flotation experiments, and simulation. In order to validate the dissolution loop method, refractory gold flotation plant conditions were selected in our Finland laboratory. The simulation results of dissolution loop flotation kinetics were compared with the actual plant mass balance. According to the comparative results, the process water created by the dissolution loop method has the same physical and chemical properties as the actual process water at the site except for SO4 -concentration. Moreover, comparing the simulation results of the experimental data and plant mass balance studies shows that the gold grade and recovery results in the simulation were lower than the actual plant mass balance.

Continuous adaptation of a digital twin model for a pilot flotation plant

Model-based methods have a key role in achieving the technical, economical, and environmental performance improvements of the mineral processing systems. However, unmodeled process phenomena and disturbances leading to unreliable modeling results, may prevent the efficient online utilization of these methods at the operational level after the deployment of the model. This study demonstrates the feasibility of online adaptation of a dynamic, mechanistic process models in mineral beneficiation application at a pilot environment. At first, a digital twin of the grinding and flotation stages of a pilot-scale plant was developed. In the experimental campaign, a change from copper-zinc-pyrite ore to a mixture of pyrite-rich and non-sulfide gangue-rich material was carried out. Thus, during experiments, a notable change in the flotation performance was observed, which could not be replicated by a constant-parameter digital twin model. The proposed parameter adaptation framework, encompassing stochastic optimization in moving time windows, was found to be suitable for finding new optimal model parameters during the changing experimental conditions by using the elemental grades in different flotation stages. In addition, simulation studies are presented to highlight the challenges of digital twin parameter adaptation in mineral processing applications, where often only a sparse and disturbance influenced data from the key process variables are available. The adaptive digital twin allows applying the dynamic, mechanistic process models efficiently for predictive simulations in operational decisions leading to more sustainable and resource-efficient minerals processing.

Insight into the Binding Mechanisms of Quartz-Selective Peptides: Toward Greener Flotation Processes.

Mining practices, chiefly froth flotation, are being critically reassessed to replace their use of biohazardous chemical reagents in favor of biofriendly alternatives as a path toward green processes. In this regard, this study aimed at evaluating the interactions of peptides, as potential floatation collectors, with quartz using phage display and molecular dynamics (MD) simulations. Quartz-selective peptide sequences were initially identified by phage display at pH = 9 and further modeled by a robust simulation scheme combining classical MD, replica exchange MD, and steered MD calculations. Our residue-specific analyses of the peptides revealed that positively charged arginine and lysine residues were favorably attracted by the quartz surface at basic pH. The negatively charged residues at pH 9 (i.e., aspartic acid and glutamic acid) further showed affinity toward the quartz surface through electrostatic interactions with the positively charged surface-bound Na+ ions. The best-binding heptapeptide combinations, however, contained both positively and negatively charged residues in their composition. The flexibility of peptide chains was also shown to directly affect the adsorption behavior of the peptide. While attractive intrapeptide interactions were dominated by a weak peptide-quartz binding, the repulsive self-interactions in the peptides improved the binding propensity to the quartz surface. Our results showed that MD simulations are fully capable of revealing mechanistic details of peptide adsorption to inorganic surfaces and are an invaluable tool to accelerate the rational design of peptide sequences for mineral processing applications.

Razvoj i primena 3D sistema za snimanje u pripremi mineralnih sirovina

Three-dimensional (3D) surface imaging systems have gained popularity in monitoring the status and condition of separation processes by characterizing the internal and external structures of particles such as size, shape, density and composition. This review article mainly focuses on demonstrating the state of the art of 3D imaging systems in mineral processing based of the recent growth in 3D sensors. The structure of this manuscript comprises an overview of the two 3D imaging systems, including RhoVol and X-ray computed tomography, based on the basic principles. Their applications in mineral processing are then explained. By integrating with other imaging tools and the 3D printing technology, 3D surface imaging systems will play an important role in the automation and control of mineral processing in the future.

Flotation Froth Image Recognition Using Vision Transformers

The application of computer vision systems on industrial flotation plants has benefited considerably from advances in deep learning over the last decade, mostly based on the use of convolutional neural networks and transfer learning. More recently, vision transformers (ViTs) have attracted strong interest since their first appearance in 2017, compared to the popular convolutional neural networks (CNNs). Although becoming well-established in many areas, they have not yet been considered meaningfully in machine vision or signal processing applications in mineral processing, despite the obvious benefits that their application could realize. In this paper, it is demonstrated that ViTs are neural network architectures highly capable of discriminating between different froth flotation images. A customized ViT model and a pretrained ViT model using transfer learning were studied and compared. The former achieved satisfactory performance and the latter achieved near perfect performance, both at a significantly lower computational cost than CNNs. These results suggest that ViTs can be a competitive alternative to CNNs in the advancement of computer vision systems on industrial flotation plants.

The molecular structure effects of starches and starch phosphates in the reverse flotation of quartz from hematite

Native starches and their phosphates with various molecular structures was introduced as the depressant to realize the flotation of quartz from hematite in this study. The present starch phosphates (WSP, NSP, GSP) were modified by the reaction between phosphate and three different corn starches (WS, NS, G50). The synthesis and characterization of starch phosphates found that starch with high amylopectin content was easily modified into starch phosphates. Microflotation tests showed that starch phosphates exhibited stronger depressing abilities of hematite flotation than native starches. Zeta potential measurement showed that both starches and starch phosphates could positively shift the zeta potential of hematite, while starch phosphates had more effects than starches. XPS and MDS indicated that the chemisorption occurred between Fe of hematite surface and CO groups of starch-based depressants. In addition, starch phosphates could adsorb onto the hematite surface through PO groups. MDS also presented that the adsorption strength of starch phosphate was mainly determined by the type and number of generating chelating rings, and the molecular structure of starch significantly affected the formation of chelate rings. The proposed adsorption model insights will significantly promote the development of starch-based depressants for iron ore flotation and other mineral processing applications.

A Review of Sensor-Based Sorting in Mineral Processing: The Potential Benefits of Sensor Fusion

Sensor-based sorting techniques offer the potential to improve ore grades and reduce the amount of waste material processed. Previous studies show that sensor-based sorting can reduce energy, water and reagent consumption and fine waste production by discarding waste prior to further processing. In this literature review, recent investigations of sensor-based sorting and the fundamental mechanisms of the main sorting techniques are evaluated to inform optimal sensor selection. Additionally, the fusing of data from multiple sensing techniques to improve characterization of the sensed material and hence sorting capability is investigated. It was found that the key to effective implementation of sensor-based sorting is the selection of a sensing technique which can sense a characteristic capable of separating ore from waste with a sampling distribution sufficient for the considered sorting method. Classes of potential sensor fusion sorting applications in mineral processing are proposed and illustrated with example cases. It was also determined that the main holdup for implementing sensor fusion is a lack of correlatable data on the response of multiple sensing techniques for the same ore sample. A combined approach of experimental testing supplemented by simulations is proposed to provide data to enable the evaluation and development of sensor fusion techniques.

Classifying grains using behaviour-informed machine learning

Sorting granular materials such as ores, coffee beans, cereals, gravels and pills is essential for applications in mineral processing, agriculture and waste recycling. Existing sorting methods are based on the detection of contrast in grain properties including size, colour, density and chemical composition. However, many grain properties cannot be directly detected in-situ, which significantly impairs sorting efficacy. We show here that a simple neural network can infer contrast in a wide range of grain properties by detecting patterns in their observable kinematics. These properties include grain size, density, stiffness, friction, dissipation and adhesion. This method of classification based on behaviour can significantly widen the range of granular materials that can be sorted. It can similarly be applied to enhance the sorting of other particulate materials including cells and droplets in microfluidic devices.

Microwave Dewatering of Gibbsite-Type Bauxite Ores: Permittivities, Heating Behavior and Strength Indices

Microwave radiation is a relatively new energy source that is being considered for several applications in mineral processing and extractive metallurgy. In the present research, various gibbsite-type bauxite ores were subjected to microwave radiation. The main objective was to assess the effect of microwave dewatering on the compressive strength indices of the ores and to compare the results obtained to those for conventional heating. Firstly, the fundamental interactions of the microwaves with the ores were evaluated by determining both the real and the imaginary permittivities as a function of temperature, and these were related to the water content. Secondly, the microwave heating behavior was modeled using a 24 factorial statistical analysis. Thirdly, the effect of dewatering by microwave heating on the compressive strength indices of roughly spherical bauxite ore pisoids was studied, and these results were compared to those obtained using conventional heating. Fourthly, the effect of particle size on the compressive strengths of irregular-shaped single particles of bauxite ore was investigated using both heating techniques. Finally, the energy requirements for dewatering of the ores, and hence reducing their compressive strengths, were compared for both processes. On the laboratory scale, the results showed that in comparison to conventional dewatering, microwave dewatering resulted in lower strength indices at both lower moisture removals and energy inputs.

Validation of Near InfraRed preconcentration strategies for ore sorting

The early rejection of gangue minerals, at coarse ore particles size (preconcentration), has been shown to be a viable option to cost reduction in many mineral processing applications. A promising technique being explored for efficient ore preconcentration is the Near InfraRed (NIR) spectroscopy. This paper attempts to validate the efficiency of near infrared preconcentration strategies, by comparing data of preconcentrated particles, when particles are scanned using near line scanner from different sides and angle of view. Three copper particles were selected from a batch of sixty preconcentrated samples, mineralogical and near infrared analysis were performed on the particles. Particles were then cut laterally (cross sectioned) and mineralogical and near infrared analysis repeated on the cut cross sectioned surface. Data of the whole samples and cross-sectioned samples are compared. Results indicate that the depth attained by scanning (both with NIR and QEMSCAN(R) of original samples is representative of each sample scanned and sufficient for preconcentration. Also, except for the differences in particle size, correlation is almost 1:1, thus, validating the initial NIR preconcentration results as being promising.

An Experimental Study of Pressure Drop Characteristics and Flow Resistance Coefficient in a Fluidized Bed for Coal Particle Fluidization

Liquid–solid fluidized beds have a wide range of applications in metallurgical processing, mineral processing, extraction, and wastewater treatment. Great interest on their flow stability and heterogeneous fluidization behaviors has been aroused in research. In this study, various fluidization experiments were performed by adjusting the operating conditions of particle size, particle density, and liquid superficial velocity. For each case, the steady state of liquid–solid fluidization was obtained, and the bed expansion height and pressure drop characteristics were analyzed. The time evolution of pressure drop at different bed heights can truly reflect the liquid–solid heterogeneous fluidization behaviors that are determined by operating conditions. With the increase in superficial liquid velocity, three typical fluidization stages were observed. Accordingly, the flow resistance coefficient was obtained based on the experimental data of bed expansion height and pressure drop. The flow resistance coefficient experiences a decrease with the increase in the modified particle Reynolds number and densimetric Froude number.

Optimizing the separation characteristics of the waterinjection hydrocyclone using mathematical modelling

Purpose. Although the hydrocyclone separator has many advantages, it still has some limitations which decrease its separation efficiency in many mineral processing applications because of fine particles which are miss separated to the coarse product in the underflow. Water injection in the conical part of the cyclone was recently implemented to solve this problem. The water injection mechanism and the way in which the injected water affects the separation are still not clear and need to be more investigated. Methods. New design of water injection hydrocyclone was tried using a modified conical part with a water injection range consist of five equal distance injection openings open directly on the periphery of the cone part. Findings. This study presents a mechanical mathematical model that simulates the water injection to give a clear indication of the injection mechanism impact on the classification process. It could also predict the dependence of the basic characteristics of the classification on the amount of the injected water and the influence of different operating and design parameters of the hydrocyclone. Originality. The model accounts for the fluid flow, the particle motion, the turbulent particle diffusion, and particle settling. Particle interactions and fine particle entrainment by settling coarse particles are also included in the model. The model was found to predict well the injection effect and agrees with the experimental results. Practical implications. The results showed also that the increase in water injection velocity leads to an increase in both the cut size and the minimal value of the separation curve. It was found also that the hydrocyclone length has an important effect on the injection process, and the separation sharpness is directly proportional to it at higher values of water injection velocity.

One-Dimensional Convolutional Auto-Encoder for Predicting Furnace Blowback Events from Multivariate Time Series Process Data—A Case Study

Modern industrial mining and mineral processing applications are characterized by large volumes of historical process data. Hazardous events occurring in these processes compromise process safety and therefore overall viability. These events are recorded in historical data and are often preceded by characteristic patterns. Reconstruction-based data-driven models are trained to reconstruct the characteristic patterns of hazardous event-preceding process data with minimal residuals, facilitating effective event prediction based on reconstruction residuals. This investigation evaluated one-dimensional convolutional auto-encoders as reconstruction-based data-driven models for predicting positive pressure events in industrial furnaces. A simple furnace model was used to generate dynamic multivariate process data with simulated positive pressure events to use as a case study. A one-dimensional convolutional auto-encoder was trained as a reconstruction-based model to recognize the data preceding the hazardous events, and its performance was evaluated by comparing it to a fully-connected auto-encoder as well as a principal component analysis reconstruction model. This investigation found that one-dimensional convolutional auto-encoders recognized event-preceding patterns with lower detection delays, higher specificities, and lower missed alarm rates, suggesting that the one-dimensional convolutional auto-encoder layout is superior to the fully connected auto-encoder layout for use as a reconstruction-based event prediction model. This investigation also found that the nonlinear auto-encoder models outperformed the linear principal component model investigated. While the one-dimensional auto-encoder was evaluated comparatively on a simulated furnace case study, the methodology used in this evaluation can be applied to industrial furnaces and other mineral processing applications. Further investigation using industrial data will allow for a view of the convolutional auto-encoder’s absolute performance as a reconstruction-based hazardous event prediction model.

The influence of microwave treatment on the mode I fracture toughness of granite

Microwave assisted mechanical rock breaking is a hybrid rock breaking method that combines microwave heating and mechanical rock breaking technologies and is proven to have potential applications in mineral processing, mining, and geotechnical engineering. It is, thus, important to understand the effect of microwave-induced damage on rock fracture characteristics. In this study, granite specimens of the Cracked Straight Through Brazilian Disc (CSTBD) were used to carry out the mode I fracture test of the samples irradiated by microwave for different time and thermal cycles. The results showed that the heating rate of the surface temperature of granite is about 1 °C/s, under microwave power of 0.9 kW. When the heating time exceeds the 60 s and after 3 cycles, the fracture toughness of the granite hardly changes with the heating time and cycles. Before 100 °C, the fracture toughness and fracture energy of granite decrease by about 40% with the increase of temperature. Under microwave irradiation, the medium absorber (biotite) in granite is heated and expanded, which results in thermal damage to the rock. Moreover, the polar materials in fluid inclusions move more violently than under conventional heat conduction heating, resulting in a lower decrepitation temperature of the inclusions than under conventional heating and decrepitation degree is more severe, which further leads to an increase in the density of microcracks in mineral crystal and a significant decrease in fracture toughness. When the surface temperature is 300 °C, the bending degree of the crack increases and deviates from the straight line obviously.

Experimental methods in chemical engineering: Zeta potential

AbstractZeta potential (ZP) is a parameter that expresses the electrochemical equilibrium between particles and liquids like in nanoparticle (NP) colloidal solutions with applications in medicine, pharmaceuticals, chemical production, mineral processing, and water and soil purification. Smoluchowski's theory applies to the ZP particles that are larger the interfacial layer but neglects surface conductivity. The Debye‐Hückel theory correctly approximates the concentration of ions in a double layer but fails to account for the dependence of ZP on the concentration of counterions. Determining ZP of NPs is essential to proper NP characterization. For instance, developing well‐defined therapeutic‐relevant nanoformulations needs information on NPs size, surface charge, stability and agglomeration behaviour. This approach has many practical challenges, from inadequate knowledge of operating standards to sampling, data interpretation and good laboratory practice for the experiments replicability. However, in drug delivery research, very little literature can provide a clear, succinct explanation of these techniques. Looking for specific guidelines to overcome frequently encountered problems during ZP measurements. This article explores factors influencing colloidal particle stability. Measurement criteria such as applied voltage, number of measurements, electrophoretic mobility (EPM), size distribution, surface shape, temperature, viscosity, particle concentration, zeta potential, nanoparticles, colloidal suspension, electrophoretic mobility, and pH.

A system of conservation laws with discontinuous flux modelling flotation with sedimentation

Abstract The continuous unit operation of flotation is extensively used in mineral processing, wastewater treatment and other applications for selectively separating hydrophobic particles (or droplets) from hydrophilic ones, where both are suspended in a viscous fluid. Within a flotation column, the hydrophobic particles are attached to gas bubbles that are injected and float as aggregates forming a foam or froth at the top that is skimmed. The hydrophilic particles sediment and are discharged at the bottom. The hydrodynamics of a flotation column is described in simplified form by studying three phases, namely the fluid, the aggregates and solid particles, in one space dimension. The relative movements between the phases are given by constitutive drift-flux functions. The resulting model is a system of two scalar conservation laws with a multiply discontinuous flux for the aggregates and solids volume fractions as functions of height and time. The model is of triangular nature since one equation can be solved independently of the other. Based on the theory of conservation laws with discontinuous flux, steady-state solutions that satisfy all jump and entropy conditions are constructed. For the existence of the industrially relevant steady states, conditions on feed flows and concentrations are established and mapped as ‘operating charts’. A numerical method that exploits the triangular structure is formulated on a pair of staggered grids and is employed for the simulation of the fill-up and transitions between steady states of the flotation column.

A fibre optic based approach and device for sensing beta radiation in liquids

An optical fibre-based beta radiation sensing device has been investigated for the purpose of detecting low levels of the daughter products of Uranium-238 in liquids for mineral processing and environmental monitoring applications. The sensor operates by detecting the scintillation light produced by polymer optical fibres. A range of fibres have been studied by quantifying a number of properties that are critical to their performance including the beta radiation scintillation response, transmission loss, and performance under the proposed environmental conditions. Based on these measurements, the most suitable fibre type was incorporated within a prototype sensing device which is capable of performing direct, real-time, continuous measurements of beta radiation emitting radionuclides within solutions or suspensions, which is not possible with current measurement techniques. The technology presented here allows for the detection of low levels of beta radiation in liquids, which could be useful for the detection of radiation in mineral processing liquids, municipal water sources, and storage tanks containing nuclear fuel.

Dynamic capture behavior of ferromagnetic particles based on fully coupled multiphysics model of particle-fluid interactions

Wet low-intensity magnetic separation (LIMS) is used in the magnetic separation of ferromagnetic minerals. The performance of the separation is controlled primarily by the internal flow in the separator, governed by the process and machine settings. However, these factors change with the properties of the magnetic material and the operating conditions. Therefore, the numerical calculation of the multiphysics coupling separation process of such dynamic captures becomes crucial for wet LIMS. In this study, we present a novel and straightforward fully coupled multiphysics modeling approach for the simulation of LIMS. In this model, particle tracing in the fluid flow module is used to calculate the location and dynamic capture behavior of ferromagnetic particles under the determined magnetic and flow fields. In particular, while calculating the magnetic force, hydrodynamic drag force, gravity, and centrifugal force, the model also inherently retains its ability to simulate the influence of ferromagnetic–particle–fluid interactions.The model is compared with experiments and the particle capture theory, and is demonstrated to be realistic for studying ferromagnetic suspensions in mineral processing applications. The model provides new possibilities to understand and predict the efficiency of mineral separation processes through detailed computational simulations of the effects of the respective operating conditions on the dynamic capture behavior of the particles.