Ore Grade Research Articles

Processing of ore from the Nud II deposit by the method of low-temperature roasting with ammonium sulfate

The Murmansk region has a significant amount of sulfide copper-nickel raw materials. Deposits of the Nud massif associated with Monchepluton are promising for processing. The Nud II deposit cut-off grade ore with an initial Ni content of 0.45 % and Cu of 0.39 % has been chosen as the research object. During the research, the ore was mixed with ammonium sulfate, grinded, and the mixture was roasted in a muffle furnace with static air atmosphere. The roasted mixture was leached in heated distilled water with constant stirring. The maximum metals recovery was achieved when crushing to a fraction of –40 µm, the ratio of ore : ammonium sulfate is equal to 1 : 7, roasting temperature 400 °C, roasting duration 300 min. In the mixture roasted at these parameters, reflexes of copper and nickel sulfates were recorded by the x-ray phase analysis (XRD) method. At the stage of water leaching, the maximum recovery was: Ni – 79.1 %, Cu – 75.8 %.

Highly Sensitive Colorimetric Determination of Os Driven by Electrocatalytic Gaseous Separation

Rapid assessment of ore grade is very important for field geological exploration, especially for precious metals. Based on the new electrooxidation gaseous separation (EOGS) idea, this paper reports a simple (colorimetric), fast (∼2 min), and accurate (μg L–1 level) method for the analysis of associated minerals Os in iron ores. The application of highly active coated electrodes has promoted the efficient and quantitative conversion of Os (including trivalent and tetravalent) to gaseous OsO4. Most of the coexisting components remain in the sample digestion solution during electrooxidation, which provides the possibility for ore sample analysis without pre-separation. Further electrochemical tests showed that appropriate catalytic activity (oxygen and chlorine evolution) was the key to both high conversion and anti-interference ability of the RuIr/Ti electrode, while long-term stability and low memory effect offered effective guarantee for practical application. Using the catalytic behavior of OsO4 to the CA-H2O2 reaction, we realized the accurate and rapid analysis of Os at the level of μg L–1 for the first time. Compared with the traditional chemical oxidation generation technology, mild operating conditions (such as not involving high temperature and strong oxidation/corrosive reagents) are the most significant characteristics of the electro-oxidation technology. Under the optimized conditions, the method has the merits of high separation efficiency (>90%), low detection limit (0.01 μg L–1), wide linear range (2–100 μg L–1), and low signal fluctuation (2.7%, n = 11). The aim of this study is to provide not only an economic and sustainable EOGS strategy for the accurate analysis of precious metal elements, but also a simple and effective rapid evaluation method for the ore grade based on the semi-quantitative analysis of color difference observed by the naked eye.

Fractal dimension used as a proxy to understand the spatial distribution for carlin-type gold deposits

The spatial distribution of ore-forming element grades typically exhibits strong nonlinear characteristics as a result of continuous geological processes. The trend of change in ore grade sequences is a crucial indicator of the degree of enrichment of an ore deposit. Therefore, quantitative evaluation of the spatial distribution of ore-forming elements holds great significance for understanding ore-forming processes as well as for ore exploration. In this work, based on the principles of fractal theory, a MATLAB-based program to calculate fractal dimensions was developed, and fractal analysis was conducted on the gold concentration values (g/t) of 25 drill holes in the Lannigou Gold Mining Area, Guizhou, China. Rescaled range analysis and the fractal correlation integral were utilized to obtain the spatial distribution of gold concentrations in the drill holes. Rescaled range analysis revealed that the Hurst exponent (HR/S) values vary in the range of 0.58–0.97, which reflected the persistent behavior of vertical variation in gold tonnage at the studied wells. Furthermore, the relationship between gold grade distribution and associated fluctuations in the fractal dimension derived from the correlation dimension (DC) was discussed. The DC value was found to vary between 1.01 and 1.59, indicating that high-grade gold deposits were distributed along steep fractal gradients and that fractal dimension values could be used as a reliable proxy to efficiently understand the migration pathways of auriferous fluid. Studies have shown that the Hurst exponent of the Au grade sequence exhibited a strong dependence on the grade of mineralization, with gold mineralized veins persisting throughout the studied drill holes. Thus, fractal dimension gradients can serve as an important proxy for identifying the locations of potential economic gold deposits in unexplored areas. Moreover, this approach can be employed to map the distribution patterns of other types of ore deposits.

Graphite ore flotation efficiency using bio ester collector formed by transesterification

Bench scale flotations were carried out to investigate an alternative collector reagent with a more sustainable bias, with selective action at the mineral interface to replace the standard hydrocarbon-base collector used for graphite’s flotation, the kerosene. A soy bio oil was produced by a transesterification reaction to perform as a collector. The study used graphite ore with 5.85% carbon content obtained from Salto da Divisa/MG region, in Bahia-Minas Province, Brazil. The design of experiments showed that the bio ester performed 11% higher than kerosene as a graphite’s collector, improving the flotation’s selectivity with 62% ore grade by bio oil and 88% recovery, against 54% ore grade using kerosene and 77% recovery. The chemical composition investigated with XPS analysis for both concentrates with kerosene and bio ester demonstrated the influence of esters and oxigenated-groups on oxidized graphite surfaces.

Limit of recovery: How future evolution of ore grades could influence energy consumption and prices for Nickel, Cobalt, and PGMs

The unique properties of certain metals have made them indispensable in manufacturing advanced technological devices and for use in the green economy. However, these metals are not infinite, and the average ore grades in mines have been decreasing in recent decades. This study examines energy consumption as a function of ore grade decline for Nickel, Cobalt, and platinum group metals (PGMs), using simulations created with HSC Chemistry software. A limit of recovery (LOR) for each commodity was also defined. A comparative analysis of the evolution of ore grades, energy costs, and market prices was additionally carried out. According to the simulations, extracting nickel from sulfide ore tailings would be profitable if the price doubled. As for Cobalt, it would only be feasible if the market price increased considerably. For PGMs, even if the ore grade reached the LOR, it would still be profitable to recover them under certain circumstances explored in the paper.

IN SITU U-Pb DATING OF GARNET AND CASSITERITE FROM THE KANBAUK W-Sn(-F) SKARN DEPOSIT, DAWEI REGION, SOUTHERN MYANMAR: NEW INSIGHTS ON THE REGIONAL Sn-W METALLOGENY IN THE SOUTHEAST ASIAN TIN BELT

Abstract Skarn ores have recently been identified beneath the historically mined placer Sn deposit at Kanbauk of the Dawei region, southern Myanmar. A large-tonnage skarn ore reserve at Kanbauk is estimated to be over 100 million tonnes, with reported ore grades of 0.17% WO3, 0.26% Sn, and 15.4% CaF2, potentially making it one of the largest W-Sn skarn deposits in the Southeast Asian tin belt. The mineralized skarns lie between marbles to the east and metasediments of the Mergui Group to the west. The timing of the mineralization is unclear, and thus the genetic relationship with regional magmatic events is not known. We report laser ablation-inductively coupled plasma-mass spectrometry U-Pb ages of garnet and cassiterite from the mineralized skarns. Garnet grains from the massive prograde skarns are typically subhedral to euhedral and show both sector and oscillatory zoning. They have 15 to 23% andradite (Ad), 55 to 67% grossularite (Gr), and 16 to 30% pyralspite (Py) (Ad15-23Gr55-67Py16-30) and contain 0.08 to 306 ppm U with a lower intercept 206Pb/238U age of 56.0 ± 1.5 Ma. Cassiterite grains from retrograde veinlets are subhedral to anhedral and have U contents from 110 to 12,000 ppm with a lower intercept 206Pb/238U age of 54.2 ± 1.7 Ma. Garnet and cassiterite have ages consistent within error and can be taken to indicate the formation of the Kanbauk W-Sn(-F) skarn deposit at around 55 Ma. Together with published ages of primary Sn-W deposits in the Dawei region, our study confirms a westwardly younging trend of mineralization toward the coast and provides support for rollback of the Neo-Tethyan subducting slab since the Late Cretaceous, which is considered as the main mechanism for the regional, extensive Sn-W mineralization.

Estimating annual average daily traffic (AADT) data on low-volume roads with the cokriging technique and census/population data

Background: Geostatistics focuses on spatial or spatiotemporal datasets. Geostatistics was initially developed to generate probability distribution predictions of ore grade in the mining industry; however, it has been successfully applied in diverse scientific disciplines. This technique includes univariate, multivariate, and simulations. Kriging geostatistical methods, simple, ordinary, and universal Kriging, are not multivariate models in the usual statistical function. Notwithstanding, simple, ordinary, and universal kriging techniques utilize random function models that include unlimited random variables while modeling one attribute. The coKriging technique is a multivariate estimation method that simultaneously models two or more attributes defined with the same domains as coregionalization. Objective: This study investigates the impact of populations on traffic volumes as a variable. The additional variable determines the strength or accuracy obtained when data integration is adopted. In addition, this is to help improve the estimation of annual average daily traffic (AADT). Methods Procedures, Process: The investigation adopts the coKriging technique with AADT data from 2009 to 2016 from Montana, Minnesota, and Washington as primary attributes and population as a controlling factor (second variable). CK is implemented for this study after reviewing the literature and work completed by comparing it with other geostatistical methods. Results, Observations, and Conclusions: The Investigation employed two variables. The data integration methods employed in CK yield more reliable models because their strength is drawn from multiple variables. The cross-validation results of the model types explored with the CK technique successfully evaluate the interpolation technique's performance and help select optimal models for each state. The results from Montana and Minnesota models accurately represent the states' traffic and population density. The Washington model had a few exceptions. However, the secondary attribute helped yield an accurate interpretation. Consequently, the impact of tourism, shopping, recreation centers, and possible transiting patterns throughout the state is worth exploring.

Estimating annual average daily traffic (AADT) data on low-volume roads with the cokriging technique and census/population data

Background: Geostatistics focuses on spatial or spatiotemporal datasets. Geostatistics was initially developed to generate probability distribution predictions of ore grade in the mining industry; however, it has been successfully applied in diverse scientific disciplines. This technique includes univariate, multivariate, and simulations. Kriging geostatistical methods, simple, ordinary, and universal Kriging, are not multivariate models in the usual statistical function. Notwithstanding, simple, ordinary, and universal kriging techniques utilize random function models that include unlimited random variables while modeling one attribute. The coKriging technique is a multivariate estimation method that simultaneously models two or more attributes defined with the same domains as coregionalization. Objective: This study investigates the impact of populations on traffic volumes as a variable. The additional variable determines the strength or accuracy obtained when data integration is adopted. In addition, this is to help improve the estimation of annual average daily traffic (AADT). Methods procedures, process: The investigation adopts the coKriging technique with AADT data from 2009 to 2016 from Montana, Minnesota, and Washington as primary attributes and population as a controlling factor (second variable). CK is implemented for this study after reviewing the literature and work completed by comparing it with other geostatistical methods. Results, observations, and conclusions: The Investigation employed two variables. The data integration methods employed in CK yield more reliable models because their strength is drawn from multiple variables. The cross-validation results of the model types explored with the CK technique successfully evaluate the interpolation technique's performance and help select optimal models for each state. The results from Montana and Minnesota models accurately represent the states' traffic and population density. The Washington model had a few exceptions. However, the secondary attribute helped yield an accurate interpretation. Consequently, the impact of tourism, shopping, recreation centers, and possible transiting patterns throughout the state is worth exploring.

Transforming iron ore processing – Simplifying the comminution and replacing reverse flotation with magnetic and gravity separation

Much of the remaining iron ore resources in Brazil consist of low-grade itabirite ores. Accordingly, a typical beneficiation circuit includes a four-staged crushing/screening plant, followed by grinding in a closed-circuit ball mill, desliming in hydrocyclones and final ore mineral concentration via multistage reverse flotation and thickening of the final product. With the need for decarbonisation in the iron and steelmaking industry, there is a growing demand for high grade iron ore concentrate at more than 67% Fe. In the context of declining ore grades, there is an increasing need for a more effective circuit for beneficiating the itabirite ore. A proposed flowsheet consisting of a primary crushing stage, SAG mill, primary concentration using a VPHGMS magnetic separator, and a final concentration stage using the Reflux Classifier was investigated. The work demonstrated the significant reduction in energy consumption, and the potential for achieving high Fe grade of more than 67%, with high recovery, and in turn considerable process simplification. The new circuit would eliminate secondary to tertiary crushing, desliming stages, complex flotation circuits, product thickening and the flotation reagent plant. Further project assessments indicated an 8% smaller footprint, and 5% CAPEX and 42% OPEX reductions compared with the conventional circuit. These improvements would lead to a 50% higher Net Present Value.

Exploring exploration—how to look for deep-sea minerals

AbstractIn the spring of 2023, the subsea industry in Norway is gearing up for an opening of the Norwegian continental shelf (NCS) for deep-sea mineral exploration and extraction. Existing research indicates that the profitability of deep-sea mining with today's technology heavily depends on high ore grades. This study explores the potential for emerging techno-operational concepts for exploration to affect the profitability of seafloor massive sulfide (SMS) industry on the NCS. By way of computer simulation, the study analyzes technologies currently or soon poised to enter the subsea market concerning their advantage or disadvantage for the SMS industry on the NCS. The study indicates a significant advantage in developing geophysical sampling technology for assessing mineral resources and some advantage in developing unmanned surface vessels for regional surveys. It further indicates that developing fleet-operated autonomous underwater vehicle concepts for high-resolution surveys is not only moot but possibly counterproductive. The study thereby contributes techno-operational insight for a budding industry currently looking for technology to improve commercial prospects.

Deployment of XRF Sensors Underground: An Opportunity for Grade Monitoring or Bulk Ore Sorting in Cave Mines

Ore grades are monitored regularly in cave mines through drawpoint sampling. Automating grade monitoring through deploying X-ray fluorescence (XRF) sensors on the buckets of production loaders has been proposed as an alternative approach to address the issues around the traditional practice of drawpoint sampling. Bucket-mounted sensors can also be employed for bulk ore sorting underground. This study is aimed at evaluating the deployment of XRF sensors on production loaders as an opportunity for grade monitoring or bulk ore sorting in caving operations. The mill feed grade prediction performances of the drawpoint sampling program and mine planning software were assessed for the Cadia East panel cave mine. The results showed that the drawpoint samples underestimated the mill feed quality during a 10-month investigation period. The cave portions with bulk ore sorting potential were linked to the extraction level layout to estimate the number of drawpoints where sensors could be situated for diverting ore and waste. Samples obtained from the mine were tested to evaluate the ability of a lab-scale proxy of a bucket-mounted XRF sensor system to measure copper and gold grades. R-squared values of 0.84 and 0.68 were achieved between the predicted and measured copper and gold grades of the samples, respectively. Sensor test results are promising in revealing the potential to utilize XRF sensors underground. Future test work is encouraged to further validate the applicability of XRF sensors in an underground mining environment.

Application of Artificial Neural Network for the Prediction of Copper Ore Grade

Precise prediction of ore grade is essential in feasibility studies, mine planning, open-pit and underground optimization, and ore grade control. Conventional methods, such as geometric and geostatistical methods, are the most popular techniques for mineral resource estimation but fail to capture the complexity of orebodies. Due to this limitation, grades are incorrectly estimated, leading to inaccurate mine plans and costly financial decisions. Here, we propose an ore grade prediction method using an artificial neural network (ANN). We collected 14,294 datasets from the Jaguar mine in Western Australia. The proposed model was developed by incorporating lithology, alteration, eastings, northwards, altitude, dip, and azimuth to predict the grade, and the performance evaluation metrics were measured based on the mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), correlation coefficient, R, and coefficient of determination (R2). The proposed ANN model outperformed classic machine learning methods with R2, R, MAE, MSE, and RMSE of 0.584, 0.765, 0.0018, 0.0016, and 0.041, respectively. The Shapley technique was used to evaluate the feature importance of the input variables for the grade prediction. Lithology demonstrated the highest influence on ore prediction, whereas eastings had the least impact on output. The proposed approach is promising for ore model prediction.

Tungsten mineralization formed by single-pulsed magmatic fluid: Evidence from wolframite-hosted fluid inclusion from the giant Dajishan “five floor” style W-polymetallic deposit

The giant Dajishan W-polymetallic deposit, located in the Nanling metallogenic belt, is a typical steeply dipping wolframite-quartz vein-type deposit. From top to bottom, the W ore grade is highest in the middle and decreases towards both ends. In No. 23 wolframite-quartz vein of the north group, the thickness of the veins increases from 0.1 ∼ 0.2 m thick in the 517 m level to a maximum of 0.6 ∼ 2.0 m in the 267 m level. According to the “Five Floor” prospecting model, the No. 23 vein corresponds to veinlet (517 m), moderate vein (467 ∼ 417 m), and thick vein zones (367 m ∼ 267 m). Primary wolframite-hosted fluid inclusions show homogenization temperatures display a slight but explicit decrease by about 20 °C in average from bottom to top, recording the essential fluid cooling process. The phase ratios (20 to 30 vol% vapor proportions) and salinities (6.3 to 10.0 wt% NaCl equiv.) are constant at all levels, indicating that neither meteoric water mixing nor fluid boiling/immiscibility is obvious during wolframite formation. The fluids contain NaCl and KCl as major salts, B as the subordinate component, with trace elements including Li, Rb, Cs, Zn, Pb, etc. The significant narrow ranges of K/Na (0.296 to 0.664), Rb/Na (0.008 to 0.018), and Cs/Na ratios (0.032 to 0.076) illustrate that wolframite ore-forming fluids come from the initial fluids derived from a highly-evolved granitic magma. In major host metasandstone, muscovitization and tourmalinization lead to the release of Na, B, and Al from the fluid into the wall rock, and liberation of Fe from the wall rock into the fluid. Wall rock composition data show that muscovitization is the main mechanism of changes in Fe contents. Preliminary calculations indicate that fluid-rock interaction may contributes minor iron to the ore-forming fluids. Combining these results, we support that “Five Floor” style W mineralization in Dajishan is formed by a single-pulsed magmatic fluid. The vertical zonation of vein thickness and ore grade in the “Five Floor” model is predominately controlled by temperature, pressure, and local tectonic regime, but it is less dependent on the fluid chemical composition.

A novel approach of utilizing the waste biomass in the magnetizing roasting for recovery of iron from goethitic iron ore

The present study investigates the recovery potential and degree of grade-upgradation for goethitic iron ore upon treatment with magnetizing roasting. Magnetizing roasting has gained interest among its peers due to its low floor/water footprint, ease of techno-economics, zero process limitations, a higher degree of grade/recovery, etc., compared with existing ore dressing methods. Magnetizing roasting comprises roasting followed by magnetic separation. In the previous studies, process is stabilized for parameters such as roasting temperature between 700 to 1000 °C, roasting time between 10 to 50 min, and in presence of 30 wt.% of coal, followed by magnetic separation with magnetic strength ranging between 1000 to 3000 Gauss. Best set of optimal recovery and iron ore grade acquired are 87.44% and 72.46% with operating conditions at (a) roasting temperature — 850 °C, (b) roasting time — 30 min, and (d) magnetic strength — 3000 Gauss. Furthermore, as environmental protection agencies are necking down and gripping the utilization of coal/coke and imposing a carbon tax, integration of carbon-neutral fuel sources like biomass and biochar has become mandatory. In accordance with this, a comparative scheme employing a variety of biomass/biochar as coke substitutes in a progressive blending mode was used to investigate the possibility of substitution. The goal is to maintain the same level of upgradation and recovery potential throughout this process. The four types of biomass/biochar used for magnetizing roasting; with rice husk char (RHC), this partial substitution campaign concluded a recovery potential of 78–80% and a goethite ore grade of 72%.

Rapid detection of molybdenum ore grade based on visible-infrared spectroscopy and MTSVD-TGJO-ELM

The rapid determination of ore grade can improve the efficiency of beneficiation. The existing molybdenum ore grade determination methods lag behind the beneficiation work. Therefore, this paper proposes a method based on a combination of Visible-infrared spectroscopy and machine learning to rapidly determine molybdenum ore grade. Firstly, 128 molybdenum ores were collected as spectral test samples to obtain spectral data. Then 13 latent variables were extracted from the 973 spectral features using partial least square. The Durbin-Watson test and the runs test were used to detect the partial residual plots and augmented partial residual plots of LV1 and LV2 to determine the non-linear relationship between spectral signal and molybdenum content. Extreme Learning Machine (ELM) was used instead of linear modeling methods to model the grade of molybdenum ores because of the non-linear behavior of the spectral data. In this paper, the Golden Jackal Optimization of adaptive T-distribution was used to optimize the parameters of the ELM to solve the problem of unreasonable parameters. Aiming at solving ill-posed problems by ELM, this paper decomposes the ELM output matrix by using the improved truncated singular value decomposition. Finally, this paper proposes an extreme learning machine method based on a modified truncated singular value decomposition and a Golden Jackal Optimization of adaptive T-distribution (MTSVD-TGJO-ELM). Compared with other classical machine learning algorithms, MTSVD-TGJO-ELM has the highest accuracy. This provides a new method for rapid detection of ore grade in the mining process and facilitates accurate beneficiation of molybdenum ores to improve ore recovery rate.

THE IMPACT OF KOKONAO STOCKPILE MATERIAL ADDITION WITH SUPER GRADE ORE MIXING MATERIAL TO THE CONSENTRATE CONTENT AND ALOY RECOVERY IN COPPER ORE FLOATATION PROCESS AT THE PT. FREEPORT INDONESIA

PT. Freeport Indonesia is one of the mining companies that process copper and gold ore. One type of ore processed in this company is Super Grade Ore (SGO). The characteristics of SGO are wet and smooth, which can result in some problems in transportation. Therefore, it is necessary to do the blending process using material from the Kokonao stockpile which tends to be drier and rougher but has a lower grade than the Cut of Grade of company. Applying the experimental method, this research aims to figure out the effects of the addition of Kokonao material in the blending process with SGO on concentrate grade and metal recovery results. Overall, the levels of Cu and Au reduce when the Kokonao material is added to each various composition, with the lowest Cu grade being 100% Kokonao (1,97%) and the highest Cu grade being 10% Kokonao (25,69%). Meanwhile, the lowest Au grade is 100% Kokonao portion (3,26 ppm) and the highest Au grade is 20% Kokonao (38,16 ppm). In addition, All various compositions result in Cu recovery that is higher than the company standard (>80%). The highest Cu recovery is the result of 50% Kokonao composition (97,12%).

Improving Graphite Ore Grade Identification with a Novel FRCNN-PGR Method Based on Deep Learning

Graphite stone is widely used in various industries, including the refractory, battery making, steel making, expanded graphite, brake pads, casting coatings, and lubricants industries. In the mineral processing industry, an effective and accurate diagnostic method based on FRCNN-PGR is proposed and evaluated, which involves cutting images to expand the dataset, combining them using the faster R-CNN model with high and low feature layers, and adding a global attention mechanism, Relation-Aware Global Attention Network (RGA), to extract features of interest from both the space and channel. The proposed model outperforms the original faster R-CNN model with 80.21% mAP and 87.61% recall on the split graphite mine dataset.

A new method for estimating ore grade based on sample length weighting

Estimation of ore grade is very important for the value evaluation of ore deposits, and it directly affects the development of mineral resources. To improve the accuracy of the inverse distance weighting (IDW) method in ore grade estimation and reduce the smoothing effect of the IDW method in grade estimation, the weight calculation method involved in the IDW method was improved. The length parameter of the ore sample was used to calculate the weight of the IDW method. The length of the ore samples was used as a new factor of the weighting calculation. A new method of IDW integrated with sample length weighting (IDWW) was proposed. The grade estimation of Li, Al, and Fe in porcelain clay ore was used as a case study. A comparative protocol for grade estimation via the IDWW method was designed and implemented. The number of samples involved in the estimation, sample combination, sample grade distribution, and other factors affecting the grade estimation were considered in the experimental scheme. The grade estimation results of the IDWW and the IDW methods were used for comparative analysis of grades of the original and combined samples. The estimated results of the IDWW method were also compared with those of the IDW method. The deviation analysis of the estimated grade mainly included the minimum, maximum, mean, and coefficient of variation of the ore grade. The estimation effect of IDWW method was verified. The minimum deviations of the estimated grade of Li, Al, and Fe were between 9.129% and 59.554%. The maximum deviations were between 4.210 and 22.375%. The mean deviations were between − 1.068 and 7.187%. The deviations in the coefficient of variation were between 3.076 and 36.186%. The deviations in the maximum, minimum, mean, and coefficients of variation of the IDWW were consistent with those of the IDW, demonstrating the accuracy and stability of the IDWW method. The more the samples involved in the estimation, the greater the estimation deviations of IDW and IDWW methods. The estimated deviations of Li, Al, and Fe were affected by the shape of the grade distribution, when the same estimation parameters were used. The grade distribution pattern of the samples significantly influenced the grade estimation results. The IDWW method offers significant theoretical advantages and addresses the adverse effects of uneven sample lengths on the estimates. The IDWW method can effectively reduce the smoothing effect and improves the utilization efficiency of the original samples.

Can ultimate recoverable resources (URRs) be assessed? Does analyzing declining ore grades help?

Inspired by a paper by Teseletso and Adachi (Miner Econ 8(10):21, 2021), the hypothesis regarding the declining grade of mined copper ore and its possible use as a guide to the future of ultimate recoverable resources (URRs) is tested. As a time axis, cumulative production is taken. Grades can be either grade of cumulative tonnage or annual production grade. Correlation can be linear (grade and tonnage) or semi-logarithmic (grade linear, tonnage logarithmic). We first show that the assumption that the highest correlation is the best guide to the future may be a fallacy. This is the linear correlation between grades of cumulative tonnage and cumulative tonnages since 1959, i.e., 85% of all copper mined historically with a near-perfect correlation approaching one (R2 = 0.97). This leads to implausibly low results of the URR, clearly demonstrating that this trend must shift in the future. Moreover, Teseletso and Adachi’s (Miner Econ 8(10):21, 2021) approach using a linear or semi-logarithmic correlation between annual grades with cumulative production leads to erroneous results. Here, the later the calculation of the correlation begins, the lower the extrapolated tonnages are at predetermined, postulated ultimate cutoff grades. This contradicts the accepted knowledge that with lower grades, the resource base is broadened—not narrowed. The only reasonable finding results from the correlation between linear grades of cumulative production with logarithmic cumulative production, i.e., the Lasky relationship, indicating a URR of 7.5 GT Cu, of which 6.7 GT remain to be mined, which is equivalent of close to 325 years of present production. The trend towards declining Cu grades with constant real Cu prices shows the potential for creative solutions for other metal as well.

Multi-Criteria Analysis for Circular Economy Promotion in the Management of Tailings Dams: A Case Study

Copper ore is a non-renewable resource with lower ore grades, which means that the extraction of more rock material is required to produce the same amount of copper, implying a greater consumption of materials, reagents, water, and energy. Since there is a greater amount of copper sulfide present in nature, concentration using the bubble flotation method will generate a greater number of tailings. This article discusses the environmental issues resulting from tailings dams and how multi-criteria decision analysis can help prioritize those sites in order to promote circular economy measures to compensate for and reduce the impacts of this type of waste generated by the copper mining industry. This work aims to contribute to this purpose by taking information from abandoned and non-active tailings, which are currently present as a result of the lack of regulations in times prior to environmental obligations and because they are metallurgical waste from old operations that had metal recovery rates that were much lower than the current rates. We propose a model based on the multi-criteria Promethee method to prioritize the tailings dams according to the commercial value of the existing materials in the deposits. A case study with an application of the model to 103 dams in the mining region of Coquimbo in northern Chile is shown.