Mineral Processing Plants Research Articles

Estimation of multi-media metal(loid)s around abandoned mineral processing plants using hyperspectral technology and extreme learning machine.

Hyperspectral techniques are promising alternatives to traditional methods of investigating potentially toxic metal(loid) contamination. In this study, hyperspectral technology combined with partial least squares regression (PLSR) and extreme learning machine (ELM) established estimation models to predict the contents of copper (Cu), zinc (Zn), arsenic (As), cadmium (Cd), lead (Pb) and tin (Sn) in multi-media environments (mine tailings, soils and sediments) surrounding abandoned mineral processing plants in a typical tin-polymetallic mineral agglomeration in Guangxi Autonomous Region. Four spectral preprocessing methods, Savitzky-Golay (SG) smoothing, continuum removal (CR), first derivative (FD) and continuous wavelet transform (CWT), were used to eliminate noise and highlight spectral features. The optimum combinations of spectral preprocessing and machine learning algorithms were explored, then the estimation models with best accuracy were obtained. CWT and CR were excellent spectral pretreatments for the hyperspectral data regardless of the applied algorithms. The coefficients of determination (R2) of estimation models for the best accuracy of various metals (loid) are as follows: Cu (CWT-ELM:0.85), Zn (CR-PLSR:0.93), As (CWT-ELM: 0.86), Cd (CR-PLSR: 0.89), Pb (CWT-PLSR: 0.75) and Sn (CR-ELM: 0.81). In contrast, ELM models had higher accuracy with R2 > 0.80 (except Cd and Pb). In conclusion, ELM-based spectral estimation models are able to predict metal (loid) concentrations with high accuracy and efficiency, providing a potential new combinatorial approach for estimating toxic metal contamination in multi-media environments.

The Use of Acid Mine Drainage (AMD) in the Flotation of a Platinum-Group-Minerals-Bearing Merensky Ore

Water scarcity is compelling mining houses to not only recycle process water but to also identify alternative sources of make-up water in concentrators. South Africa has significant volumes of acid mine drainage (AMD) generated from vast mining operations. This study investigated the viability of using AMD as a replacement for potable water in the flotation of a platinum-group-minerals (PGM)-bearing Merensky ore. Rougher and cleaner flotation testwork was conducted at laboratory scale to compare the performances of potable water (baseline water), AMD treated with Ca(OH)2, and AMD treated with the Veolia process. Water analysis showed that the three water types differed in pH, water hardness, conductivity, and total dissolved solids. The results showed the AMD treated with Ca(OH)2 was detrimental to PGM recovery compared to potable water at depressant dosages of 50 g/t. Specifically, AMD treated with Ca(OH)2 achieved a PGM rougher recovery of 67.8%, while potable water achieved a PGM rougher recovery of 88.4%. Depressant dosage optimisation and treatment of the AMD using the Veolia process were investigated as potential strategies to mitigate the detrimental effects of the AMD treated with Ca(OH)2 on the flotation performance of a Merensky ore. The AMD treated with the Veolia process achieved a PGM rougher recovery of 70.8%. Thus, treatment of the AMD was beneficial, though the PGM and base metal sulphides (BMS) recoveries were still lower than those achieved in potable water. Reducing the depressant dosage to 25 g/t in AMD treated with Ca(OH)2 resulted in the highest PGM, Cu, and Ni rougher recoveries of 91%, 60.2%, and 58%, respectively. The AMD treated with Ca(OH)2 at lower depressant dosage outperformed the potable water in terms of PGM and BMS recoveries and concentrate grades, indicating that AMD has the potential to replace potable water as make-up water in Merensky ore processing plants. The results showed that depressant optimisation is important to achieve superior metallurgical results when using AMD treated with Ca(OH)2. The use of AMD in Merensky ore processing plants not only conserves freshwater in minerals processing plants but also reduces high volumes of contaminated effluents.

Mineralogical Properties of the Copper Slags from the SarCheshmeh Smelter Plant, Iran, in View of Value Recovery

Annually, hundreds of thousands of tons of slags are involved in the reverberator and flash smelting as well as converting operations of Cu-Fe sulfide concentrates to produce matte in the Sar Cheshmeh copper smelter plant, Iran, disposed in the landfill and cooled in air. Due to their relatively high average copper content (about 1.5 wt%), a mineral processing plant based on the flotation process has recently been established to produce thousands of tons of Cu-sulfide concentrate after slag crushing and fine grinding operation. In order to make the flotation process more efficient, more knowledge is required on the form and origin of the copper losses in the slag. To achieve this, mineralogical studies of the slags using optical microscopy, X-ray diffraction (XRD), and scanning electron microscopy (SEM) methods have been carried out. Mineralogical analyses showed the main part of copper losses into the semi- to fully-crystallized magnetite-rich reverberator and flash slags characterized by crystal–glass matrix ratio ≤ 1 is moderate to coarse particles of Cu-Fe sulfides, i.e., chalcopyrite (CuFeS2) and bornite (Cu5FeS4), that are mainly chemically entrapped. In contrast, the mechanically entrapped fine- to coarse-grain (from 20 up to 200 µm) spherical-shaped of high-grade matte particles with chalcocite (Cu2S) composition containing droplets or veinlets of metallic copper (Cu0) are the dominant forms of copper losses into the converter slags characterized by crystal–glass matrix ratio > 1. From the value recovery point of view, our result show that the fully crystallized slags containing moderate- to coarse-grain copper-bearing particles will result in efficient recovery of a significant amount of entrained copper due to better milling response compared to semi-crystallized ones due to locking the fine- to moderate-grain copper particles in the silicate glassy matrix. Laboratory-scale grinding experiments showed that normal (≤74 μm) to fine (≤44 μm) grinding of high- Cu grade slags lead to a significant increase in the liberation degree of copper particles. in contrast, the increase in fine particle fractions (<37 μm) due to re-grinding or ultra-fine grinding of the originally low-Cu grade slags does not lead to the liberation of copper particles, but it will reduce the efficiency of the flotation process. This study suggests that the highest rate of copper recovery of the slag by the flotation process will be obtained at particle size 80% passing 44 µm which has also reached the optimal liberation degree of copper-bearing particles.

A 3D cellular automata ore stockpile model – Part 1: Simulation of size segregation

This paper describes a 3D cellular automaton (CA) for modelling ore pile formation that incorporates size segregation due to surface stratification. Ore stockpiles and bins are essential in mining operations as a buffer between the mine and the mineral processing plant, and their operation plays an important role in the overall performance of the downstream equipment. Size segregation can occur if the feed size to the pile varies over time or due to a variety of segregation mechanisms occurring in the pile itself. In particular,it can occur as a result of percolation stratification within surface flows. The structure of the newly developed CA model is described, and the simulated surface profile and size segregation response is validated through a series of laboratory-scale piles for characterizing the segregation potential of the feed particles. It is found that the model adequately describes the segregation behaviour at a range of model scales where coarse particles roll to the outside of the pile and fine particles are concentrated around the centre of the pile. Moreover, it is found that the model is sufficiently fast to use in real-time applications such as dynamic process control and digital twins.

Recovery of Hematite from Banded Hematite Quartzite of Southern India by Magnetic Separation and Reverse Flotation

Recovery and grade are the two crucial performance parameters commonly used in mineral processing plant operations. These two parameters are interdependent. An increase in recovery would result in a decreased product grade and vice versa. The present study enumerates concentration efficiency (CE),which can be adopted exclusively for processing low-grade hematite ore by WHIMS—the reverse flotation route to produce a pellet grade concentrate. In this study, the ore’s amenability by wet high-intensity magnetic separation followed by the reverse flotation of a magnetic concentrate route is investigated on BHQ samples of the Sandur schist belt (Kumaraswamy hills), India, after its characterization by microscopic and XRD studies. Dodecyl amine acetate was used as a collector to float siliceous gangue while depressing hematite using the freshly synthesized caustic starch as a depressant. The separation efficiency of the flotation was evaluated by estimating the grade, recovery, and concentration efficiency. The WHIMS conducted using the feed with the particle size minus 106 µm (d80 = 82 µm) followed by reverse flotation produced a pellet grade concentrate assaying 64.60% Fe, a 0.32 alumina-to-silica ratio with 60.4% Fe recovery, and a yield of 37.4% with 79.0% concentration separation efficiency.

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.

Machine Learning Technique for Recognition of Flotation Froth Images in a Nonstable Flotation Process

The paper is focused on the analysis of the relation between the stability of the flotation process and the efficiency of Machine Learning (ML) algorithms based on the flotation froth images. An ML process should enable researchers to construct Artificial Intelligence (AI) algorithms for flotation process control. The image of the flotation froth includes information characterizing the flotation process. The information can be extracted with the aid of the Image Recognition (IR) algorithms based on the ML. This enables construction of a flotation process control system in the mineral processing plant, which is based on the recognition of images of the flotation froth. The IR algorithms do not provide stable image recognition results and are not efficient in the situation where the parameters of the flotation process are highly unstable. The classification results were equal to 75.11% and 69.62% for a stable and unstable process, respectively. The experimental data collected at the Polish Pb/Zn mineral processing plant provided better insight to the relationships between the flotation process parameters and ML efficiency. These relationships were analyzed, and guidelines for the construction of the ML process for flotation process control have been formulated.

Avoiding Buffer Tank Overflow in an Iron Ore Dewatering System with Integrated Control System

High water usage is necessary while ore passes through the many stages of a mineral processing plant. However, a dewatering system filters the final ore pulp product to remove the water, which is reutilized in the previous processes. This step is fundamental to reducing the fresh new water consumption. Usually, several tanks, pumps, and filters form a dewatering system—any failure or shutdowns from those components disbalance the pulp flow. The waste of many tons of water and ore products for a tailing dam is the worst consequence of a mass disbalance in a dewatering system. This paper proposes an advanced regulatory control strategy composed of cascade and override loops for a dewatering system. The main purpose is to increase the production period, even under filter failure and changes in the inlet pulp characteristics. This control strategy is evaluated using a digital model of a large-scale Brazilian iron ore processing plant. Two scenarios are investigated: the simultaneous failure of two filters and disturbances in the flow and density of the thickener. The simulation results show that the proposed control strategy could extend the period of operation of the dewatering plant under failures in the disc filters and reject significant disturbances. For the considered simulation period, the proposed solution increases the time to overflow by 72% when compared to the previous control strategy. Thus, it is possible to avoid the waste of approximately 2448.36 tons of ore pulp that would be sent to the tailings dam.

Energy, exergy and thermoeconomic analysis of an industrial solar pond

This study evaluates the viability of an industrial salinity gradient solar pond during two operating seasons (2014 and 2015). The Granada solar pond was built to supply low-temperature heat (up to 60 °C) to satisfy the temperature requirements of the flotation unit in a mineral processing plant (Solvay Minerales in Granada (Spain)). Energy analysis indicates that the non-convective zone (NCZ) and the upper convective zone (UCZ) have low efficiencies because these zones have a low capacity to store heat, and no heat extractions are carried out from these zones. In contrast, heat extraction in the lower convective zone (LCZ) has a positive impact on the system because it increases the capacity of the solar pond to store energy. Higher efficiencies were achieved in the second operation period in the LCZ, since higher amount of heat was extracted during this period. The overall exergy efficiency of the LCZ after the first and second operation periods was 1.6% and 2.3%, respectively. The solar pond works at temperatures close to room temperature and thus the exergetic efficiency decreases significantly. Regarding the thermoeconomic analysis, the cost of exergy stored is studied in this work, using two different approaches, by estimating the minimum price for exergy stored or the minimum surface area that ensure the thermoeconomic viability. The price of the stored exergy tends to decrease inversely to the price of fuel oil. Thus, the cost of the stored exergy must be four to five times higher than the price of fuel oil for the solar pond system to be feasible. On the other hand, solar pond technology is affected by economies of scale; the larger the solar pond, the higher the inflation rates and the lower the cost reduction rates that can be accommodated. This implies that estimating the cost of the stored exergy is complex due to the variability of the processes involved and can lead to erroneous interpretations when evaluating the viability of a solar pond from a thermoeconomic perspective.

Risk Assessment of Mining Environmental Liabilities for Their Categorization and Prioritization in Gold-Mining Areas of Ecuador

Mining environmental liabilities (MEL) are of great concern because of potential risks to ecosystems and human health. In this research, the environmental risk (RI) related to MEL existing in three artisanal and small-scale gold-mining areas of Ecuador was evaluated. For this purpose, data of 167 MEL including landfills, mining galleries, tailing deposits, and mineral processing plants from Macuchi, Tenguel–Ponce Enriquez, and Puyango mining areas, were analyzed. The risk assessment related to the presence of waste deposits was carried out based on the methodology proposed by the Spanish Geological Survey. Moreover, the procedure outlined in the Environmental Risk Assessment Guide of the Ministry of Environment of Peru for nonwaste deposits was applied. The highest RI values were identified in Puyango and Tenguel–Ponce Enriquez. Thus, they were both categorized as priority control areas requiring intervention and rehabilitation plans. The MEL that require a high level of intervention include waste deposits and mine entrances associated with potentially toxic elements. Moreover, the point risk maps showed that rivers in the studied areas have a potential pollution risk. This study provides risk levels associated with MEL in mining areas from Ecuador. This information could be used for environmental management and pollution mitigation.

Low-grade chalcopyrite ore, heap leaching or smelting recovery route?

Chalcopyrite, the major mineral source of copper, is commonly treated pyrometallurgically, using froth flotation, smelting, converting and electrorefining processes. The ore treatment however is limited to the cut-off grade concentration below which the extraction is not economically viable. In this study a comparison of feasibility of the pyrometallurgical and hydrometallurgical treatment of low grade (0.23%–0.35% Cu), high content chalcopyrite (>90% CuFeS2) ore were studied from both technical and economic viewpoints. On a technical stance, the results of a pilot test on froth flotation of 100 t of low-grade ore are presented. The pilot test results confirm the generation of a Cu concentrate of a grade range 23.3–19.6% Cu with a copper recovery in the range 90.9–78.1%. For the hydrometallurgical treatment, bio heap leaching of 40,000 t of low-grade ore using agglomeration and aeration was proven to achieve a copper recovery of 32.6% for the chalcopyrite portion of the ore over 326 days of operation.On the economic side, the operating cost of the process per tonne of copper cathode for both processes were reported. The operating cost for the pyrometallurgical (PM) processing plant was calculated to be 1.4 times less than the hydrometallurgical (HM) processing plant. The sales revenue of the PM route was also higher than that of the HM route due to the byproducts credit. Capital cost estimates were made assuming that the current solvent extraction/electrowinning (SX/EW) facilities in a HM route and the current smelting and electrorefining plants in the PM route have enough free capacity to absorb the upstream outputs. Applying the copper extraction efficiencies of the pilot plants, the profitability indicators for both routes in treatment of 3000,000 t ore/year was studied. The impact of the interest rate was found significant in economic preference of the HM route over the PM route. This is due to the high capital cost of the mineral processing plant despite of the higher operating cost of the HM plant. The capital cost of the mineral processing plant can make the HM route feasible at interest rates higher than 27.5%. At discount rates lower than 27.5%, the PM route process is viable over the HM route at copper market price of US$9500/t. A drop in copper selling price to US$6000/t acts in favor of the HM route, making it feasible at interest rates higher than 22.4%. For a general scenario where the construction of SX/EW and smelter/refinery plants are required, the discount rate at which the net present value (NPV) of the two routes are equal has decreased from 27.5% to 17%. In this scenario, for discount rates higher than 17%, the HM route with 31% total copper recovery is more feasible than the PM route with 79% recovery.

Beneficial effects of a polysaccharide-based grinding aid on magnetite flotation: a green approach

Grinding is the most energy-intensive step in mineral beneficiation processes. The use of grinding aids (GAs) could be an innovative solution to reduce the high energy consumption associated with size reduction. Surprisingly, little is known about the effects of GAs on downstream mineral beneficiation processes, such as flotation separation. The use of ecofriendly GAs such as polysaccharide-based materials would help multiply the reduction of environmental issues in mineral processing plants. As a practical approach, this work explored the effects of a novel polysaccharide-based grinding aid (PGA) on magnetite's grinding and its reverse flotation. Batch grinding tests indicated that PGA improved grinding performance by reducing energy consumption, narrowing particle size distribution of products, and increasing their surface area compared to grinding without PGA. Flotation tests on pure samples illustrated that PGA has beneficial effects on magnetite depression (with negligible effect on quartz floatability) through reverse flotation separation. Flotation of the artificial mixture ground sample in the presence of PGA confirmed the benefits, giving a maximum Fe recovery and grade of 84.4 and 62.5%, respectively. In the absence of starch (depressant), PGA resulted in a separation efficiency of 56.1% compared to 43.7% without PGA. The PGA adsorption mechanism was mainly via physical interaction based on UV–vis spectra, zeta potential tests, Fourier transform infrared spectroscopy (FT-IR), and stability analyses. In general, the feasibility of using PGA, a natural green polymer, was beneficial for both grinding and reverse flotation separation performance.

Advantages of selective grinding using high-pressure grinding rolls combined with air classification

It is well known that the use of high-pressure grinding rolls (HPGR) in crushing circuits improves subsequent power consumption through the output of finer material: the yield of fine fractions in the HPGR product is higher than that in the product of traditional crushers (including third and even fourth stage crushing). It has been established that the use of screens in HPGR circuits is inadvisable and would limit the circuit capacity due to lower friction and the fact that screen performance depends on the size of the finished product. In addition, the use of water (its availability and cost) for spiral classifiers, screens, or hydrocyclones remains a major problem in the industry. The alternative option involving air classification has been used in the cement industry for several decades to separate material fractions in the size range of 0.01–1.0 mm, but has yet to find its way to mineral processing plants. It has been shown that volumetric fracturing in HPGR improves subsequent selective separation. Adequate distribution of the ore components in air classifiers allows the separation boundary to be optimized. The expediency of using HPGR in combination with air classification has been substantiated as it allows, in some cases, to eliminate subsequent ball grinding at industrial plants. This is revolutionizing the ore preparation process. The authors are grateful to Köppern for their research support.The work was carried out with the support of the Russian Foundation for Basic Research (project No. 20-55-12002).

Wastewater Treatment in Mineral Processing of Non-Ferrous Metal Resources: A Review

Water used by mining enterprises needs to be comprehensively recovered and utilized to achieve clean production. This requires the effective treatment of mineral processing wastewater. Wastewater produced during non-ferrous metal mineral processing contains a complex mixture of pollutants at high concentrations, making comprehensive treatment difficult. Here, the sources of and hazards posed by wastewater produced during non-ferrous metal mineral processing are introduced and the techniques for removing heavy metal ions and organic chemicals are reviewed. Chemical precipitation and adsorption methods are often used to remove heavy metal ions. Chemical precipitation methods can be divided into hydroxide and sulfide precipitation methods. Organic chemicals are mainly removed using oxidation methods, including electrochemical oxidation, photocatalytic oxidation, and ultrasonic synergistic oxidation. External and internal cyclic utilization methods for treating wastewater produced by mineral processing plants are introduced, and a feasibility analysis is performed.

Digitalization Solutions in the Mineral Processing Industry: The Case of GTK Mintec, Finland

The technologies used in mineral process engineering are evolving. The digital mineral processing solutions are based on advances in our ability to instrumentally measure phenomena at several stages of the beneficiation circuit, manage the data in real-time, and to analyze these data using machine learning to develop the next generation of process control. The main purpose of this study is to overview various digital solutions for mineral processing plants and characterization laboratories while emphasizing their utilization in the current state of the digitization process of the GTK Mintec. This study highlights the specialized digital technologies that are particularly relevant for mineral processing and beneficiation. The digital solutions studied in this article include digital twin, machine vision, information management system, sensors, smart equipment, machine learning techniques, process control system, robotic cell, and Internet of Things applied across the whole chain of studying materials from the mineralogical examinations through the bench-scale studies to the pilot test trials. The aim is to provide a clear view on the different aspects of digitizing mineral processing plants based upon the lessons learned from the development plans in GTK Mintec.

Estimation of Recovery Percentage in Gravimetric Concentration Processes using an Artificial Neural Network Model

The concentrate process is the most sensitive in mineral processing plants (MPP), and the optimization of the process based on intelligent computational models (machine learning for recovery percentage modelling) can offer significant savings for the plant. Recent theoretical developments have revealed that many of the parameters commonly assumed as constants in gravity concentration modelling have a dynamic nature; however, there still lacks a universal way to model these factors accurately. This paper aims to understand the model effect of operational parameters of a jig (gravimetric concentrator) on the recovery percentage of the interest mineral (gold) through empirical modeling. The recovery percentage of mineral particles in a vibrated bed of big particles is studied by experimental data. The data used for the modelling were from experimental test in a pilot-scale jig supplemented by a two-month field sampling campaign for collecting 151 tests varying the most significant parameters (amplitude and frequency of pulsation, water flow, height of the artificial porous bed, and particle size). It is found the recovery percentage (%R) decreases with increasing pulsation amplitude (A) and frequency (F) when the size ratio of small to large particles (d/D) is smaller than 0.148. An empirical model was developed through machine learning techniques, specifically an artificial neural network (ANN) model was built and trained to predict the jig recovery percentage as a function of operation parameters and is then used to validate the recovery as a function of vibration conditions. The performance of the ANN model was compared with a new 65 experimental data of the recovery percentage. Results showed that the model (R2 = 0.9172 and RMSE = 0.105) was accurate and therefore could be efficiently applied to predict the recovery percentage in a jig device.

Economic model predictive control of a high-pressure grinding rolls circuit: energy considerations

Mineral processing plants remain nowadays infamous for their low energy efficiency. Economic model predictive control (EMPC), by directly considering the energy costs, could possibly help reducing their footprint, but its environmental benefits are yet to be quantified. In an attempt to cast some light on this topic, this manuscript compares EMPC to advanced regulatory control (ARC) in scenarios with identical ore hardness disturbance sequences using the net value and the specific energy as metrics. The simulated circuit comprises a high-pressure grinding rolls (HPGR), a ball mill, and a flotation circuit. It is based on population balance modelling and is an extension of previous work. The ARC system consists of proportional-integral controllers maximizing the plant feed rate with override constraint handling. The results show that 1) The EMPC cost criterion must contain weights on the changes of the manipulable variables to ensure stability, 2) advanced regulatory control can generate the same economic performance as EMPC because the system's constraints define the economic optimum, and 3) the EMPC can minimize the specific energy by saturating the HPGR circuit circulating load with a hybrid criterion that penalizes power draw.

Analysis of Occupational Accidents in the Spanish Mining Sector in the Period 2009-2018.

Occupational accidents in the Spanish mining industry have been substantially reduced over the last decades. However, the incidence rate shows higher values than other leading mining countries. In this regard, the research carried out reveals the factors influencing the high incidence rates of the Spanish mining sector, based on three scenarios: underground mining (UG), quarries and open pit mining (OP) and mineral processing plants (PP). The three most common types of accident for each scenario have been determined, considering the accidents in Spain between 2009 and 2018. The analysis also includes the main deviations, and physical activities that the injured worker was carrying out at the time of the accident. Besides, a model to predict the number of accidents based on the lost working days is also presented together with the incidence and severity risk index adjusted by the number of employees and their worked hours, respectively, in each scenario. These finding can be relevant to define the most effective measures and policies to reduce the number of accidents in the mining sector.

Modeling the Process of Thawing of Tailings Dam Base Soils by Technological Waters

The storage of wastes from mining and mineral processing plants in the tailing dumps in regions with cold climates has a number of environmental consequences. Interactions of water with tailings in cold climates often lead to the thawing of permafrost soils, formation of technogenic thawing zones, and leakage of drainage waters. In the case of fault zones development in these areas, technogenic solutions are often filtered outside the tailing dump, promoting further development of filtration channels. In order to prevent leakage of solution from tailing dumps over time, it is necessary to determine the thawing zones and prevent the formation of filtration channels. In the case of the formation of a filtration channel, it is necessary to know what rate of rock thawing occurred near the formed filtration channel. In this study, for the tailing dump of a diamond mining factory, we calculated two exothermic effects: (1) due to physical heating of dump rock by filtering industrial water with temperatures from 2 to 15 °C through the rock; and (2) due to the chemical interaction of industrial water with the dam base rock. The amount of energy transferred by the water to the frozen and thawed rock over 10 years was calculated using thermophysical modeling and was 207.8 GJ and 8.39 GJ respectively. The amount of energy that the rock received during the ten-year period due to dissolution of the limestones and equilibration of solutions was calculated using thermodynamic modeling and was 0.37 GJ, which is 4.4% of the average amount of energy, expended on heating the thawed rock (8.39 GJ).

Application of Wavelet Filtering to Vibrational Signals from the Mining Screen for Spring Condition Monitoring

The main task of mineral processing plants is to further process the raw material extracted in the mining faces into a concentrate with the highest possible concentration of the final product. In practice, it is a complex process in which several stages can be distinguished. After the ore has been transported to the surface by the skip shaft, one of the first steps is sieving the ore, which is typically performed using vibrating mining screens. In a typical Ore Enrichment Plant, the screening process is carried out by several such machines. This is a typical bottleneck in the technological chain. For this reason, the main challenge for users is to achieve the highest reliability and efficiency of these technical facilities. The solution is to focus on predictive maintenance strategies based on the development of monitoring and advanced diagnostic procedures capable of estimating the time of safe operation. This work was developed as part of an advanced diagnostic system ensuring comprehensive technical conditioning and early fault detection of components such as the engine, transmission, bearings, springs, and screen. This article focuses on vibration data. The problem of damage detection in the presence of periodically impulsive components resulting from falling feed material on the screen and its further screening process has been considered. These disturbances are of a non-Gaussian noise nature, the elimination of which is essential to extract the fault-related signal of interest. One solution may be to properly smooth and filter the raw signal. In this article, a wavelet filtering technique is applied. First, the wavelet filtering procedure is described. In the next step, the performance of a wavelet filter is investigated depending on its parameters. Then, the results of wavelet filtering are compared with such methods as low-pass filtering and smoothing using a moving average. Finally, the impact of wavelet filtering on the calculation of screen trajectories is investigated.