Copper Mine Research Articles

Adaptive Weighted Multi-kernel Learning for Blast-Induced Flyrock Distance Prediction

AbstractIn the field of civil and mining engineering, blasting operations are widely and frequently used for rock excavation, However, some undesirable environmental problems induced by blasting operations cannot be ignored. Blast-induced flyrock is one important issue induced by blasting operation, which needs to be well predicted to identify the blasting zone’s safety zone. This study introduces an adaptive weighted multi-kernel learning model (AW-MKL) to provide an accurate prediction of blast-induced flyrock distance in Sungun Copper Mine site. The proposed model uses a combination of multi-kernel learning (MKL) approach and adaptive weighting strategy based on weighted Euclidean distance and modified local outlier factor (MLOF) to maximally improve the predictive ability of kernel ridge regression (KRR). To demonstrate the superiority of the proposed approach, six machine learning models were developed as comparisons, i.e., KRR, RF, GBDT, SVM, M5 Tree, MARS and AdaBoost. The outcomes of the proposed method achieved the highest accuracy in testing phase, with RMSE of 2.05, MAE of 0.98 and VAF of 99.92, which confirmed the strong predictive capability of the proposed AW-MKL in predicting blast-induced flyrock distance.

Prediction and minimization of blasting flyrock distance, using deep neural networks and gravitational search algorithm, JAYA, and multi-verse optimization algorithms

Flyrock represents a significant and fundamental challenge in surface mine blasting, carrying inherent risks to humans and the environment. Consequently, accurate prediction, minimization, and identification of the factors influencing flyrock distance are imperative for effective control and mitigation of its destructive consequences. Machine learning and artificial intelligence methodologies have emerged as viable means to predict and simulate in different scientific fields. This study employs Deep Neural Network in conjunction with three optimization algorithms including the JAYA Algorithm, Multi-Verse Optimization Algorithm, and Gravitational Search Algorithm to predict blasting flyrock distance. The developed model consists of a combination of seven input parameters, encompassing both blasting design parameters and rock geomechanical properties. The output of the Deep Neural Networks model is the flyrock distance. For the training and testing of the model, a dataset comprising of 245 blasting records, collected from Songun copper mine, Iran, was utilized. The DNN model yielded an R2 value of 0.96 and an MSE value of 34.11. These results demonstrate the high accuracy and predictive capability of the model. Furthermore, the application of three optimization algorithms resulted in similar optimized parameter values, which minimized flyrock distances.

The interface between research funding and environmental policies in an emergent economy using neural topic modeling: Proposals for a research agenda

AbstractResearchers have been calling for further analysis of the relationships between academia and policy making. Since several studies are conducted with resources from public funding agencies, it is essential to assess whether governments have been supporting research that can contribute to future environmental policies and whether research has been supporting the elaboration of public policies. To fill these gaps, the objective of this study was to evaluate the similarities and gaps between research and environmental policies. A bibliometric analysis of studies funded by Chilean agencies and text analytics techniques were employed to analyze research and environmental policies. We used BERTopic, a topic modeling technique that generates coherent topic representations. Then, topics obtained from the two sets of documents (research and policies) were compared. This study found that sustainability, life‐cycle assessment, climate change, and ecosystem services are central research themes in the country. The topic modeling analysis showed that research has been concentrated on the areas of “Sustainable Fisheries and Aquaculture Management” and “Sustainable Bioenergy Production and Chemical Processes.” The topics most frequently present in the environmental policies were “Environmental Education for Children: Awareness and Care,” “Bird Conservation and Habitat Importance in Chile,” and “National Biodiversity Conservation and Sustainable Use Strategy.” The greatest gaps are observed in the clusters of “Sustainable Bioenergy Production and Chemical Processes” and “Copper Mining and Mineral Processing Techniques” (prevalent in research) and in “Anatomy and Physiology of Aquatic and Terrestrial Organisms” and “Biogeography and Taxonomy of Southern Hemisphere Insects” (prevalent in policy).

Effect of slope angle on fractured rock masses under combined influence of variable rainfall infiltration and excavation unloading

Intense precipitation infiltration and intricate excavation processes are crucial factors that impact the stability and security of towering and steep rock slopes within mining sites. The primary aim of this research was to investigate the progression of cumulative failure within a cracked rock formation, considering the combined effects of precipitation and excavation activities. The study was conducted in the Huangniuqian eastern mining area of the Dexing Copper Mine in Jiangxi Province, China. An engineering geological investigation was conducted, a physical model experiment was performed, numerical calculations and theoretical analysis were conducted using the matrix discrete element method (MatDEM), and the deformation characteristics and the effect of the slope angle of a fractured rock mass under different scenarios were examined. The failure and instability mechanisms of the fractured rock mass under three slope angle models were analyzed. The experimental results indicate that as the slope angle increases, the combined effect of rainfall infiltration and excavation unloading is reduced. A novel approach to simulating unsaturated seepage in a rock mass, based on the van Genuchten model (VGM), has been developed. Compared to the vertical displacement observed in a similar physical experiment, the average relative errors associated with the slope angles of 45°, 50°, and 55° were 2.094%, 1.916%, and 2.328%, respectively. Accordingly, the combined effect of rainfall and excavation was determined using the proposed method. Moreover, the accuracy of the numerical simulation was validated. The findings contribute to the seepage field in a meaningful way, offering insight that can inform and enhance existing methods and theories for research on the underlying mechanism of ultra-high and steep rock slope instability, which can inform the development of more effective risk management strategies.

Analysis of Water Quality of Le'an River in Poyang Lake Basin Based on CCME-WQI Method

As the largest freshwater lake in China, Poyang Lake plays a key role in supporting the balance of aquatic ecosystems, and the water quality of its inlet rivers affects the lake's water quality. Le'an River, a typical inlet river of Poyang Lake, was selected as the research object. Based on the water quality data of six monitoring points in the upper, middle, and lower reaches of the mainstream of Le'an River from 2012 to 2020, the CCME-WQI method was used to evaluate the water quality of the river after systematically analyzing the spatiotemporal variation of the concentration of pollutants in the mainstream of the river. Finally, the main influencing factors of the water quality of the river were extracted and analyzed according to the PCA method. The results showed that： ① The water volume upstream and downstream of the river was more seriously polluted in the pre-study time period, which was due to the presence of Dexing Copper Mine in the upstream and small and medium-sized mines and farmland downstream. ② Before 2017, the water volume downstream of Le'an River had the worst water quality, with TP and NH4+-N exceeding the standard rate of 43.3% and 85.0%, respectively, and the lowest WQI mean value of 86.2. After 2017, due to the effective management of pollutant discharges in the watershed, the water volume downstream of the river improved significantly and continued to be in an excellent state, and the mean value of the WQI reached 100.0. ③ The factors influencing the water quality of the mainstem of the Le'an River could be divided into four categories： human activities, seasonal factors, atmospheric deposition of pollutants, and the physical and chemical properties of the water volume itself, with human activities being the dominant factor for water quality changes at Dawuhekou and Shizhenjie, whereas the seasonal factors had the greatest influence at the remaining locations. ④ Organic matter pollution was obvious in the upper and lower Le'an River water volume, and the water volume at Dawuhekou was mainly affected by nearby mining activities, whereas the water volume at Shizhenjie was mainly affected by agriculture. Le'an River had serious organic matter pollution downstream before 2017, and mining and agricultural activities in the watershed had a high degree of impact on water quality. The treatment of mineral processing wastewater should be upgraded, and the discharge of pollutants from agriculture in the downstream of the watershed should be regulated.

Suitability of the Former Mamut Copper Mine for Nature-based Tourism: A Preliminary Visual Assessment of Site Conditions

The Former Mamut Copper Mine (MCM) is a historical landmark rich in both aesthetic and cultural values, but from a scientific perspective, its potential for nature-based tourism remains unexplored. A visual rapid assessment was conducted at MCM, to examine its suitability for nature-based tourism, focusing on site accessibility, hazard levels, scenic visibility, and scenic beauty. Twenty locations with high aesthetic and/or historical values were visually investigated, based on the four specified criteria, utilizing a three-point rating score system. The assessment emphasized a moderate suitability level for nature-based tourism at MCM (Score = 2.337), with significant variations in suitability level observed between the 20 locations (χ2 = 32.36, p = 0.021). Twelve locations showing higher scores in site accessibility, hazard level, and scenic visibility were determined to be more suitable for nature-based tourism, when compared to the other eight locations. Both the Principal Component Analysis and Spearman’s Correlation test highlighted the significant influences of hazard level (ρ = 0.685, p < 0.001), accessibility (ρ = 0.652, p = 0.002), and scenic visibility (ρ = 0.685, p < 0.001) on the suitability of MCM for nature-based tourism, in comparison to site scenic beauty (ρ = 0.203, p = 0.381). These findings provide new insights highlighting the high suitability of MCM for nature-based tourism. Additionally, this research uncovers safety concerns, rendering public access and the carrying out of recreational activities at MCM, currently infeasible. Management authorities are urged to prioritize efforts that reduce hazard levels and enhance the quality and accessibility of local attractions in future.

Profile of Bacterial Communities in Copper Mine Tailings Revealed through High-Throughput Sequencing.

Mine-tailing dumps are one of the leading sources of environmental degradation, often with public health and ecological consequences. Due to the complex ecosystems generated, they are ideal sites for exploring the bacterial diversity of specially adapted microorganisms. We investigated the concentrations of trace metals in solid copper (Cu) mine tailings from the Ovejería Tailings Dam of the National Copper Corporation of Chile and used high-throughput sequencing techniques to determine the microbial community diversity of the tailings using 16S rRNA gene-based amplicon sequence analysis. The concentrations of the detected metals were highest in the following order: iron (Fe) > Cu > manganese (Mn) > molybdenum (Mo) > lead (Pb) > chromium (Cr) > cadmium (Cd). Furthermore, 16S rRNA gene-based sequence analysis identified 12 phyla, 18 classes, 43 orders, 82 families, and 154 genera at the three sampling points. The phylum Proteobacteria was the most dominant, followed by Chlamydiota, Bacteroidetes, Actinobacteria, and Firmicutes. Genera, such as Bradyrhizobium, Aquabacterium, Paracoccus, Caulobacter, Azospira, and Neochlamydia, showed high relative abundance. These genera are known to possess adaptation mechanisms in high concentrations of metals, such as Cd, Cu, and Pb, along with nitrogen-fixation capacity. In addition to their tolerance to various metals, some of these genera may represent pathogens of amoeba or humans, which contributes to the complexity and resilience of bacterial communities in the studied Cu mining tailings. This study highlights the unique microbial diversity in the Ovejería Tailings Dam, including the discovery of the genus Neochlamydia, reported for the first time for heavy metal resistance. This underscores the importance of characterizing mining sites, particularly in Chile, to uncover novel bacterial mechanisms for potential biotechnological applications.

Heavy Metal Contamination of Surface Sediments-Soil Adjoining the Largest Copper Mine Waste Dump in Central India Using Multivariate Pattern Recognition Techniques and Geo-Statistical Mapping

This detailed study assessed heavy metal contamination of sediments/soil near central India’s largest copper mining area using 38 sampling sites within 10 km of the mine using atomic absorption spectroscopy. This study utilized multivariate pattern recognition methods, namely hierarchical clustering analysis (HCA) and principal component analysis (PCA), for source identification. Twelve parameters, i.e., copper (Cu), manganese (Mn), cobalt (Co), zinc (Zn), nickel (Ni), lead (Pb), organic matter (OM), cation exchange capacity (CEC), soil pH, distance (D), and elevation (E) were analyzed. The hierarchical cluster analysis (HCA) was used to analyze the sample sites with similar metal contamination and principal component analysis (PCA) was used to analyze the relationship between the parameters as well as to identify sources of heavy metal pollution. Three major pollution hotspots were detected by AHC and were classified as unpolluted/low pollution sites (UPS: mean concentration factor of 1.35 for Cu), highly polluted sites (HPS: mean concentration factor of 22 for Cu), and extremely polluted sites (EPS: mean concentration factor of 74 for Cu). PCA revealed three hidden factors/components, namely PC1 (explaining 38% of the variability), PC2 (18% of the variability), and PC3 (14% of the variability). Metals showed strong positive loading in PC1, explaining the highest variability. The mean content of Cu in soil/sediment samples was 502.526 mg/kg. The mean copper content was 10 times higher than the natural crustal value of 45mg/kg, indicating severe pollution in several sites around the study area. Mapping of copper contamination was conducted to reveal the spatial distribution of copper contamination using QGIS. This study exposes the heavy metal contamination level in surface sediments/soil and the effectiveness of pattern recognition techniques for the assessment of multivariate datasets in discerning spatial disparities and identifying the contamination causes.

Performance of stabilized copper mine tailings with freeze-thaw and wet-dry seasonal cycles

Approximately 3.44 billion tons of copper mine tailings (MT) were produced globally in 2018 with an increase of 45% from 2010. Significant efforts are being made to manage these tailings through storage facilities, recycling, and reuse in different industries. Currently, a large portion of tailings are managed through the tailing storage facilities (TSF) where these tailings undergo hydro-thermal-mechanical stresses with seasonal cycles which are not comprehensively understood. This study presents an investigative study to evaluate the performance of control and cement-stabilized copper MT under the influence of seasonal cycles, freeze-thaw (F-T) and wet-dry (W-D) conditions, representing the seasonal variability in the cold and arid regions. The control and cement-stabilized MT samples were subjected to a maximum of 12 F-T and 12 W-D cycles and corresponding micro-and-macro behavior was investigated through scanning electron microscope (SEM), volumetric strain (εv), wet density (ρ), moisture content loss, and unconfined compressive strength (UCS) tests. The results indicated the vulnerability of Copper MT to 67% and 75% strength loss reaching residual states with 12 F-T and 8 W-D cycles, respectively. Whereas the stabilized MT retained 39%–55% and 16%–34% strength with F-T and W-D cycles, demonstrating increased durability. This research highlights the impact of seasonal cycles and corresponding strength-deformation characteristics of control and stabilized Copper MT in cold and arid regions.

Assessment of Cement Mortar Strength Mixed with Waste Copper Mine Tailings (CT) by Applying Gradient Boosting Regressor and Grid Search Optimization Machine Learning Approach

Growing environmental concerns and resource scarcity, the construction industry must investigate sustainable materials that reduce waste while improving building material properties. This study investigates the viability of using waste copper mine tailings (CT) as a partial replacement for river sand in cement mortar, assessing the impact on mechanical strength and developing a predictive model using a Gradient Boosting Regressor (GBR) and Grid Search Optimization (GSO). Copper tailings mix designs ranging from 0% to 50% replacement by volume (river sand) were developed, with a constant cement quantity while varying the proportions of sand and tailings. The experiments were carried out at Poornima University in Jaipur, using standard protocols to prepare specimens and measure their compressive strength for 0% CT to 50% CT as volume percent in the mixture. The results showed that the addition of copper tailings up to 20% (3CT2) had highly increased the mortar strength, while mix design 6CT3 showed the best strength at 30% CT. Beyond this threshold, strength declined, thus indicating an optimal replacement level. In the final step of the research, the GBR-GSO-based machine learning approach was employed for developing the predictive model for compressive strength in mortar with different contents of copper tailing for mix types 3CT and 6CT. The predictions obtained by the developed model were in very good agreement with empirical data, thus supporting the potential of machine learning to predict material performance for guiding the use of unconventional additives in construction. It could be said that this work not only represents the material properties of copper-tailing-infused mortar but also showcases state-of-the-art machine learning techniques in the building materials science domain while opening new paths for more innovative and sustainable building practices.

Reassessing Bronze Age Metallurgy in Upland Southwest China on the Basis of Excavations at Longbohe, Yunnan

Longbohe is a newly discovered copper mining, smelting and production site located strategically beside the Red River on the China–Vietnam border in Southeast Yunnan, China. Recent excavations have dated it from the second half of the second millennium to the end of the first millennium BC, making it the earliest copper mining and processing site in Upland Southwest China and Southeast Asia. The metallurgical production tradition in Upland Southwest China and Southeast Asia is recognized and detailed with reference to the chaîne opératoire revealed at the Longbohe site. The location of Longbohe provides evidence for a route along which metallurgy was introduced from Southwest China into Southeast Asia. Current evidence further suggests that metallurgy probably arrived on the Yunnan–Guizhou Plateau along the eastern Qinghai–Tibet Plateau corridors, and then entered Southeast Asia along south-flowing rivers. The Longbohe site is thus a most significant find in the southward dissemination of metallurgical technological systems into Southwest China and Southeast Asia in the later second millennium BC.

Initial pedogenic processes, mineral and chemical transformations and mobility of trace elements in Technosols on dumps of the former copper mines in Miedziana Góra and Miedzianka, the Świętokrzyskie Mts., south-central Poland

Technosols develop, among others, on dumps of former copper mines which became overgrown in spontaneous plant succession. The aim of the study was to determine the effect of pedogenesis on soil properties, mineral and geochemical composition, as well as operationally defined forms of As, Cd, Cr, Cu, Fe, Mn, Ni, Pb and Zn in Technosols developed on former mine dumps of the Miedziana Góra and Miedzianka former mining areas in south-central Poland. The studied Technosols were weakly developed soils with a simple soil profile comprising (1) C horizons in the subsoil and (2) O and A (or AC) horizons in the topsoil. The pHH2O of soils was 5.5–8.1. High soil pHH2O values were due to occurrence of carbonates (up to 25 %). The highest content of total organic carbon (TOC) and total nitrogen (TN) was found in O horizons as well as in A and AC horizons including buried A horizon. Soil magnetic susceptibility (χ) was 6.0–171.3 × 10–8 m3 kg−1. In each profile, the highest χ values were typical of A horizons including the buried A horizon. The most common minerals present in all profiles were quartz, kaolinite and mica. There were also admixtures of carbonates (dolomite, calcite), feldspars (orthoclase and albite), jarosite, goethite and traces of Fe sulphides. SEM-EDS analyses showed that soil substrate was subject to transformations of sulphides into Fe oxides. Moreover, pedogenic Cu carbonates were found in pores and on coarse materials. Technosols were enriched in Ag, As, Cd, Co, Cu, Hg, Mo, Ni, Pb, Sb, Zn, U and Th. The most mobile trace elements in the studied Technosols were Cd, Cu and Zn. Topsoil horizons of the studied soils contained a considerable share of Pb, Cu, Zn and Ni bound with soil organic matter. Arsenic was mostly bound with Fe oxides. First indicators of pedogenesis in the studied Technosols are (1) accumulation of soil organic matter in the topsoil followed by the decrease of soil pH and leaching of carbonates, (2) mineral transformations in the soil substrate (alteration of sulphides into Fe oxides and jarosite; origin of pedogenic Cu carbonates due to crystallization from soil solutions), (3) increase of magnetic susceptibility in the topsoil which may be an effect of atmospheric dust fall out or pedogenic transformation of Fe oxides into the phases with higher magnetic susceptibility and (4) the effect of soil properties, mineral composition and soil organic matter on geochemical forms of potentially toxic trace elements. The novelty value of the study lies in the identification of pedogenic Cu carbonates and the potential effect of soil-forming processes on the increase of topsoil magnetic susceptibility.

Molybdenum Recovery from the Copper Hydrometallurgical Extraction Route with High Content of Chloride Ions Using the Ion Exchange Technique

This manuscript describes molybdenum recovery from copper Pregnant Leaching Solutions (PLSs) in the copper oxide mining industry with high contents of chloride ions. This product was recovered from the copper leaching pond solutions of the Chilean National Copper Mining Corporation (CODELCO) using the ion exchange process. This process recovered molybdenum from initial Mo concentrations of 50 mg/L using two different anion−exchange resins. The first experiments, with 1 g/L Cl−, achieved recovery yields of 90% and molybdenum concentrates as CaMoO4. However, the process was permanently halted because of the early saturation of the ion exchange resins given by high chloride concentrations (10 g/L Cl−) of the original copper PLS and the significant reagent consumption given by the low adsorption capacity. Static studies were developed to determine the adsorption isotherms, followed by continuous molybdenum recovery tests. The Langmuir adsorption parameters were determined as a function of the chloride concentration, giving absorption capacities from 180 to 250 mg Mo/gram of resin and recoveries from 63% to 90%. The breakthrough values for the DOWEX and Lewatit resins for chloride contents of 10 g/L were 180 and 245 BV, respectively, while for 1 g/L, these values were 620 and 890 BV. This allowed obtaining elution solutions of 890 mg Mo/L for the DOWEX resin and 1000 mg Mo/L for the Lewatit resin.

Metallography of tailings from the Mansfeld copper mining area

Abstract The copper slate deposit of the Mansfeld copper mining area was mined in the 19th and 20th century. Mineral collectors can still find traces of copper smelting activities on the slag heaps of the various copper smelters. A piece from the Krug smelter slag heap mainly consists of Cu and Fe sulfides. The piece in question might be the intermediate product copper matte. The pieces from Hettstedt and the August Bebel smelter contain clearly visible metallic copper. The Hettstedt specimen might be a piece of furnace lining with adhering slag. Cr and Mg could be found, suggesting that chromium magnesia was used as refractory material. The piece from the August Bebel smelter contains up to 18 wt. % Cu. It does, however, not contain any sulfur. It is most likely Fe2SiO4 slag (fayalite) with a high proportion of FeO. The presence of Sn and Zn suggests that this slag was formed during the processing of bronze or brass.

Comparison of artificial refrigeration cooling schemes for high temperature tunneling roadway in deep mines--the case of Yunnan Dahongshan Copper Mine

With the increasing depth of metal mine mining, the heat damage in deep mine tunneling roadway has become more and more serious, in order to effectively reduce the temperature in high temperature tunneling roadway, a mobile artificial refrigeration equipment was developed. To improve the efficiency of cold-heat exchange of air, enhance the cooling effect of the refrigeration equipment, evaluate the applicability of refrigeration equipment and determine the optimal cooling scheme, field tests under different conditions were conducted in Dahongshan Copper Mine in Yunnan Province. Additionally, numerical simulation studies were carried out by Fluent in order to analyze the temperature distribution characteristics of the roadway under different conditions. The results show that: (1) The mobile artificial refrigeration equipment can reduce the wet bulb temperature in the cooling space to below 30 °C, meeting the requirements of the "Safety regulation for metal and nonmetal mines" (GB16423-2020), and condition 4 has the best cooling effect, which can reduce the wet bulb temperature in the cooling space to below 23.64 °C; (2) The addition of cold air supply duct, wind barriers and heat exhaust ducts enhances the cooling effect in the cooling space; (3) The highest average axial temperature of the roadway is observed at the location of 6m from hot air outlets of the refrigeration equipment or heat exhaust ducts outlets, and the highest temperature is about 9 °C higher than the initial ambient temperature of the roadway; (4) The cold air in the cooling space gradually contracts towards the cold source outlet as the distance from the working face increases. The research results can provide guidance for the prevention and control of heat damage and the development of artificial refrigeration equipment for high temperature tunneling roadway in metal mines.

Assessment of Heavy Metal Contamination and Its Impact on Water Quality and Aquatic Life in Mine Surface Plant Areas

The Copper mining industry accounts for the country’s largest export earning and creates several jobs. Despite this the mines have been known to be the major contributor to the environmental pollution. It has been observed that in one province of the country, there is high presence of iron and other heavy metals in the surrounding areas. Unfortunately these heavy metals find themselves in water bodies and consequently affect the aquatic life. This study was conducted to develop suitable machine learning prediction models that estimate the impact of mine pollutants on fish production in the Kalumbila area of North-Western Province. The Machine Learning techniques employed include Multiple Linear Regression (MLR), Artificial Neural Networks (ANN), Random Forest (RF) and K-Nearest Neighbors (KNN). These models were evaluated and, in terms of Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) with the values of 0.25 (25%) and 0.22 (22%) indicating that Random Forest appear to be the best-performing models in terms of prediction accuracy compared to other models. In addition, the RF model also achieved the highest R2 score of 0.94, indicating its ability to explain a greater proportion of the variance in the dependent variable compared to the other models. This means that RF provides a strong prediction accuracy than other models in terms of determining heavy metal contamination in impact on Water Quality and Aquatic Life in Mine Surface Plant Areas. Therefore this study shows the potential of Machine Learning models to assist decision makers in understanding the pollution levels in water bodies.

Experimental study on different phytoremediation of heavy metal pollution in HDS sediment of copper mines

Experimental study on different phytoremediation of heavy metal pollution in HDS sediment of copper mines

An application of simultaneous stochastic optimisation on an open-pit copper mining complex with supply, recovery, and market uncertainties

ABSTRACT A mining complex is an integrated value chain that consists of multiple mines, stockpiles, processors, waste dumps, and tailing facilities, and where the in-situ materials mined are transformed into products sold to customers. Simultaneous stochastic optimisation provides a strategic mine planning framework that incorporates all components of a mining complex into a single mathematical representation, capitalising on the synergies between components while considering uncertainty. An application of this optimisation framework at a coper mining complex demonstrates that the incorporation of material supply, market demand, and processing recovery uncertainties into the modelling leads to production schedules that yield a higher NPV and metal recovery.

Revitalizing post-communist urban industrial areas: Divergent narratives in the imagining of copper mine reopening and tourism in a Romanian town

The post-communist period has strongly impacted the former communist Romania's urban industrial areas. Recent debates regarding urban industrial towns in Central and Eastern Europe reveal that their governance has been prone to neglect and that a lack of jobs has led to depopulation, seasonal migration, and a change in local labour skills. Drawing on 20 in-depth interviews with community leaders in Moldova Nouă, Romania, this study presents divergent narratives on the development of the local economy around the reopening of a copper mine and tourism. The findings suggest that, given the town's excellent position on the River Danube and its proximity to the Danube Gorges, tourism is perceived as a long-term solution. However, to date, tourism has not proved to be a major economic engine for the local population but could be of great interest for further local development. On the other hand, although copper mining proved polluting in communist times, some respondents prefer reopening the mines to have better paid jobs.

A Systems Engineering Approach to Incorporate ESG Risks and Opportunities in Early-Stage Mine Design and Planning

This study examines how ESG risks and opportunities can be systematically identified, assessed, and incorporated into the early-stage design and planning of natural resources projects. The focus for this study will be on the mining activities required to source the resources for the global decarbonization effort. The need for a framework to incorporate ESG risks and opportunities into the strategic mine planning process was first identified in the de Beers Sustainability Valuation Approach. The Social Value Capital Decision Model advanced by BHP represents an advance on the de Beers model. This is the first example of a structured methodology for systematically considering stakeholder values and incorporating these into the capital decision framework. To test the applicability of a new approach to mine design by using Quality Function Deployment (QFD), a case study involving a copper mine located in South America was developed. This case study demonstrates how QFD can provide clear line-of-sight to connect design decisions with priority stakeholder concerns. The framework provides a communications tool for aligning the ESG design process across functional silos within complex organizations. The development of appropriate software tools could assist in managing the inherent complexity associated with integrating stakeholder value concerns into early stage design decisions.