Hasil untuk "Mineral industries. Metal trade"

William Xu, Amir Eskanlou, Mansur Arief et al.

The global capacity for mineral processing must expand rapidly to meet the demand for critical minerals, which are essential for building the clean energy technologies necessary to mitigate climate change. However, the efficiency of mineral processing is severely limited by uncertainty, which arises from both the variability of feedstock and the complexity of process dynamics. To optimize mineral processing circuits under uncertainty, we introduce an AI-driven approach that formulates mineral processing as a Partially Observable Markov Decision Process (POMDP). We demonstrate the capabilities of this approach in handling both feedstock uncertainty and process model uncertainty to optimize the operation of a simulated, simplified flotation cell as an example. We show that by integrating the process of information gathering (i.e., uncertainty reduction) and process optimization, this approach has the potential to consistently perform better than traditional approaches at maximizing an overall objective, such as net present value (NPV). Our methodological demonstration of this optimization-under-uncertainty approach for a synthetic case provides a mathematical and computational framework for later real-world application, with the potential to improve both the laboratory-scale design of experiments and industrial-scale operation of mineral processing circuits without any additional hardware.

Shigenobu Hirose, Patrick Stengel, Natsue Abe et al.

The third ``Mineral Detection of Neutrinos and Dark Matter'' (MD$ν$DM'25) meeting was held May 20-23, 2025 in Yokohama, Japan, hosted by the Yokohama Institute for Earth Sciences, Japan Agency for Marine-Earth Science and Technology (JAMSTEC). These proceedings compile contributions from the workshop and update the progress of mineral detector research. MD$ν$DM'25 was the third such meeting, following the first in October of 2022 held at the IFPU in Trieste, Italy and the second in January of 2024 hosted by the Center for Neutrino Physics at Virginia Tech in Arlington, USA. Mineral detectors record and retain damage induced by nuclear recoils in synthetic or natural mineral samples. The damage features can then be read out by a variety of nano- and micro-scale imaging techniques. Applications of mineral detectors on timescales relevant for laboratory experiments include reactor neutrino monitoring and dark matter detection, with the potential to measure the directions as well as the energies of the induced nuclear recoils. For natural mineral detectors which record nuclear recoils over geological timescales, reading out even small mineral samples could be sensitive to rare interactions induced by astrophysical neutrinos, cosmic rays, dark matter and heavy exotic particles. A series of mineral detectors of different ages could measure the time evolution of these fluxes, offering a unique window into the history of our solar system and the Milky Way. Mineral detector research is highly multidisciplinary, incorporating aspects of high energy physics, condensed matter physics, materials science, geoscience, and AI/ML for data analysis. Although realizing the scientific potential of mineral detectors poses many challenges, the MD$ν$DM community looks forward to the continued development of mineral detector experiments and the possible discoveries that mineral detectors could reveal.

Iye Szin Ang, Martin Johannes Findl, Elisabeth Hauzinger et al.

Automated rock classification from mineral composition presents a significant challenge in geological applications, with critical implications for material recycling, resource management, and industrial processing. While existing methods using One dimensional Convolutional Neural Network (1D-CNN) excel at mineral identification through Raman spectroscopy, the crucial step of determining rock types from mineral assemblages remains unsolved, particularly because the same minerals can form different rock types depending on their proportions and formation conditions. This study presents a novel knowledge-enhanced deep learning approach that integrates geological domain expertise with spectral analysis. The performance of five machine learning methods were evaluated out of which the 1D-CNN and its uncertainty-aware variant demonstrated excellent mineral classification performance (98.37+-0.006% and 97.75+-0.010% respectively). The integrated system's evaluation on rock samples revealed variable performance across lithologies, with optimal results for limestone classification but reduced accuracy for rocks sharing similar mineral assemblages. These findings not only show critical challenges in automated geological classification systems but also provide a methodological framework for advancing material characterization and sorting technologies.

Ivanović Aleksandra, Apostolovski-Trujić Tatjana, Tasić Viša et al.

This paper analyzes the amount and chemical composition (As, Cd, Pb) of total atmospheric deposition (TAD) in Bor from 2016-2023. The analysis results for three measurement sites in the urban environment of Bor (Technical Faculty - TF, City Park - TP, and Institute of Mining and Metallurgy - IN) are presented. In the observed period, four exceedances of the maximum permitted TAD concentration of 450 mg·m-2·day-1 were recorded in monthly samples at the TF location, and two at the TP location. At the IN location, 8 exceedances were recorded in 2022-2023, when construction works were carried out near this measuring point. In the observed period, the average pH value of TAD for all measuring points was 7.6. At all observed locations, the presence of a very strong (r> 0.8) and strong (0.8>r>0.6) correlation (Pearson's) between As, Cd, and Pb detected in TAD was observed, which indicates the common origin of these elements.

Vuković Boško

Due to the movement of blocks on the overburden and coal, on the southern slope of field "C", where the main coal layer, with optimal quality characteristics, was exploited as fuel for TE Gacko, the exploitation of coal in the Central exploitation zone was completely suspended. Coal supply for the Gacko thermal power plant, from April 2024, is made exclusively from roof coal seams. The upper strip level (12 Ng) is represented by the increased participation of tailings in the coal layer with coal interlayers of small thickness, whose exploitation is difficult with mining machinery, due to the low coefficient of utilization of exploitation reserves. Smaller layers of coal are separated by packages of stratified tailings of greater thickness, so the exploitation of these coal reserves would be necessary for a longer period and would not provide continuity in the safe supply of energy fuel to the thermal power plant. The only variant of using the coal reserves of the upper belt level is in combination with the middle coal layer in the deep layer, with a high degree of availability of mining machinery. For the calculation of coal reserves of the upper belt level, all strata that meet the heat value of the thermal power plant were taken collectively, and coal strata of low calorific value were excluded because they create negative effects during combustion in the boiler of the thermal power plant (increased slag and ash).

Nikolić Violeta, Staletović Novica, Presburger-Ulniković Vladanka et al.

The paper analyzed the content of pollutants and harmful elements in the soil in Bor during the year 2022. The concentrations of Cu, Ni, Cr, Pb and As in the soil were determined at 15 locations near the Bor Mining and Smelting Complex. Soil pollution at individual locations was assessed based on the measured concentration of elements and certain factors and pollution indices (contamination factor (Cf), degree of contamination (Cd), modified degree of contamination (mCd), geo-accumulation index (Igeo), pollution load index (PLI), Nemerow Pollution Index (PIN), individual and total index of potential risk of environmental pollution). Pearson's correlation coefficients indicated a strong correlation between Cu and Pb (0.887), Cu and As (0.953), Pb and As (0.960) and Cr and Ni (0.889). The results showed that there is an uneven distribution of pollutants and harmful elements in the investigated area. Elevated content of the mentioned elements was determined mostly for Cu, which is expected, and only at one location for Pb and As. Based on the obtained indices and factors, the overall quality of the soil in the selected locations is variable. It was found that the sites varied from uncontaminated to heavily polluted at the Park location - the old center of Bor, which had the highest risk of pollution.

Dimtrijević Stevan, Dimitrijević Silvana, Vurdelja Borislava

The electrodes of Ni-Cd batteries contain heavy metals such as Ni, Cd and Co, which must be recycled to solve environmental problems. At the same time, Ni and Co obtained during the process have significant economic value and belong to strategically important metals (Critical Raw Materials according to the EU classification). The paper presents a literature review of procedures for recycling Ni-Cd batteries with a special reference to leaching with sulfuric acid as the most commonly used procedure. This simple hydrometallurgical technology is ideal for small and medium-capacity plants because it does not require the expensive equipment used in pyrometallurgical technology that applies carbothermal reduction at a high temperature. The paper also presents the results of the physicochemical characterization of the battery after manual disassembly, performed by ICP OES and SEM-EDS analyses.

Jayanta Bhattacharya

The adoption of Artificial Intelligence (AI) is driving three core fundamental shifts: the creation of cognitive supply chains through predictive logistics and demand forecasting; the establishment of algorithmic pricing and risk management that fundamentally alters trading strategies and hedging; and the rise of AI-enabled sustainability (Green Metal Tracking), which links production data to ESG compliance for value realisation. AI is the definitive competitive differentiator, separating agile, data-centric metal firms from legacy operators globally. The global energy and power industry is undergoing a fundamental and non-linear transformation driven by the widespread adoption of AI. These include the transition to Cognitive Grids, where AI enables the real-time, seamless integration of intermittent renewable energy sources (solar, wind) into the grid via hyper-accurate forecasting and dynamic load balancing. Secondly, AI is enabling predictive, Autonomous Operations across generation, transmission, and distribution, transitioning the sector from reactive maintenance to zero-downtime environments. Critically, AI is enabling the development of new business models, such as Energy-as-a-Service and dynamic pricing, fundamentally altering the utility-consumer relationship. The global Oil and Gas (O and G) industry, encompassing Upstream, Midstream, and Downstream sectors, is undergoing a fundamental shift from a traditional, risk-heavy, and reactive business model to an AI-enabled, autonomous, and predictive enterprise. This transformation is driven by AI’s unique ability to process the industry’s vast, heterogeneous datasets (seismic, telemetry, sensor) at speed. The core fundamental changes identified across academic and industry sources include the transition to Autonomous Field Operations through agentic AI in drilling and production; the systemic De-risking of the Upstream Sector via AI-powered geological and seismic data interpretation; and the creation of Cognitive Supply Chains and Trading that utilise predictive models for dynamic demand forecasting, pipeline flow optimisation, and risk management. Key transformations include the emergence of autonomous quarry operations, AI-driven supply chain optimisation, the transformation of business models towards data-as-a-service, and the profound impact of AI on global productivity and trade dynamics. The article surmises that AI is not merely an incremental tool but a foundational technology reshaping the value creation structure across multiple sectors globally.

Zi Wang, Xingcheng Xu, Yanqing Yang et al.

We propose a deep learning framework, DL-opt, designed to efficiently solve for optimal policies in quantifiable general equilibrium trade models. DL-opt integrates (i) a nested fixed point (NFXP) formulation of the optimization problem, (ii) automatic implicit differentiation to enhance gradient descent for solving unilateral optimal policies, and (iii) a best-response dynamics approach for finding Nash equilibria. Utilizing DL-opt, we solve for non-cooperative tariffs and industrial subsidies across 7 economies and 44 sectors, incorporating sectoral external economies of scale. Our quantitative analysis reveals significant sectoral heterogeneity in Nash policies: Nash industrial subsidies increase with scale elasticities, whereas Nash tariffs decrease with trade elasticities. Moreover, we show that global dual competition, involving both tariffs and industrial subsidies, results in lower tariffs and higher welfare outcomes compared to a global tariff war. These findings highlight the importance of considering sectoral heterogeneity and policy combinations in understanding global economic competition.

Sneha Latha Kommuguri, Smrutishree Pratihary, Thangjam Rishikanta Singh et al.

Unlike junctions in solid-state devices, a plasma-metal junction (pm-junction) is a junction of classical and quantum electrons. The plasma electrons are Maxwellain in nature, while metal electrons obey the Fermi-Dirac distribution. In this experiment, the current-voltage characteristics of solid-state devices that form homo or hetero-junction are compared to the pm-junction. Observation shows that the turn-on voltage for pn-junction is 0.5V and decreases to 0.24V for metal-semiconductor junction. However, the pm-junction's turn-on voltage was lowered to a negative value of -7.0V. The devices with negative turn-on voltage are suitable for high-frequency operations. Further, observations show that the current-voltage characteristics of the pm-junction depend on the metal's work function, and the turn-on voltage remains unchanged. This result validates the applicability of the energy-band model for the pm-junction. We present a perspective metal-oxide-plasma (MOP), a gaseous electronic device, as an alternative to metal-oxide-semiconductor (MOS), based on the new understanding developed.

Peter Klimek, Maximilian Hess, Markus Gerschberger et al.

The steel industry is a major contributor to CO2 emissions, accounting for 7% of global emissions. The European steel industry is seeking to reduce its emissions by increasing the use of electric arc furnaces (EAFs), which can produce steel from scrap, marking a major shift towards a circular steel economy. Here, we show by combining trade with business intelligence data that this shift requires a deep restructuring of the global and European scrap trade, as well as a substantial scaling of the underlying business ecosystem. We find that the scrap imports of European countries with major EAF installations have steadily decreased since 2007 while globally scrap trade started to increase recently. Our statistical modelling shows that every 1,000 tonnes of EAF capacity installed is associated with an increase in annual imports of 550 tonnes and a decrease in annual exports of 1,000 tonnes of scrap, suggesting increased competition for scrap metal as countries ramp up their EAF capacity. Furthermore, each scrap company enables an increase of around 79,000 tonnes of EAF-based steel production per year in the EU. Taking these relations as causal and extrapolating to the currently planned EAF capacity, we find that an additional 730 (SD 140) companies might be required, employing about 35,000 people (IQR 29,000-50,000) and generating an additional estimated turnover of USD 35 billion (IQR 27-48). Our results thus suggest that scrap metal is likely to become a strategic resource. They highlight the need for a massive restructuring of the industry's supply networks and identify the resulting growth opportunities for companies.

Julia Kamml, Claire Acevedo, David Kammer

The mineralized collagen fibril is the main building block of hard tissues and it directly affects the macroscopic mechanics of biological tissues such as bone. The mechanical behavior of the fibril itself is determined by its structure: the content of collagen molecules, minerals, and cross-links, and the mechanical interactions and properties of these components. Advanced-Glycation-Endproducts (AGEs) cross-linking between tropocollagen molecules within the collagen fibril is one important factor that is believed to have a major influence on the tissue. For instance, it has been shown that brittleness in bone correlates with increased AGEs densities. However, the underlying nano-scale mechanisms within the mineralized collagen fibril remain unknown. Here, we study the effect of mineral and AGEs cross-linking on fibril deformation and fracture behavior by performing destructive tensile tests using coarse-grained molecular dynamics simulations. Our results demonstrate that after exceeding a critical content of mineral, it induces stiffening of the collagen fibril at high strain levels. We show that mineral morphology and location affect collagen fibril mechanics: The mineral content at which this stiffening occurs depends on the mineral's location and morphology. Further, both, increasing AGEs density and mineral content lead to stiffening and increased peak stresses. At low mineral contents, the mechanical response of the fibril is dominated by the AGEs, while at high mineral contents, the mineral itself determines fibril mechanics.

Francesco Pappone, Federico Califano, Marco Tafani

Accurately determining the geographic origin of mineral samples is pivotal for applications in geology, mineralogy, and material science. Leveraging the comprehensive Raman spectral data from the RRUFF database, this study introduces a novel machine learning framework aimed at geolocating mineral specimens at the country level. We employ a one-dimensional ConvNeXt1D neural network architecture to classify mineral spectra based solely on their spectral signatures. The processed dataset comprises over 32,900 mineral samples, predominantly natural, spanning 101 countries. Through five-fold cross-validation, the ConvNeXt1D model achieved an impressive average classification accuracy of 93%, demonstrating its efficacy in capturing geospatial patterns inherent in Raman spectra.

Tasić Viša, Apostolovski-Trujić Tatjana, Radović Bojan et al.

In addition to traffic, the main source of nitrogen dioxide is also industrial plants where combustion takes place at high temperatures, such as heating plants and smelters. Nitrogen oxides from traffic are the dominant component of air pollution in large cities and represent an important source of exposure and health risk for people, especially those who move along busy roads. In Bor, real-time nitrogen dioxide concentrations have been measured since 2010 at the measuring point "Institute IRM Bor" near Mining and Metallurgy Institute Bor. This paper presents an analysis of the results of measuring nitrogen dioxide concentrations in Bor in the periods 2010-2013 and 2019-2023. Based on the analysis of nitrogen dioxide measurement results, it was established that in both periods there are seasonal changes (heating/non-heating season) in nitrogen dioxide concentrations. Nitrogen dioxide concentrations were on average 17% higher in the heating season than in the non-heating season. Also, in the period 2019-2023 the average concentration of nitrogen dioxide was 28.6 µg/m3 , which is about 18% more compared to the period 2010-2013 in which the average concentration of nitrogen dioxide was 24.3 µg/m3 . In the observed periods, there was no exceedance of the limit value for the average annual concentration of nitrogen dioxide of 40 µg/m3 . However, several days were recorded with an average daily concentration of nitrogen dioxide above the daily limit value of 85 µg/m3 .

S. M. Aldoshin

The current state of the mineral resource base of Russia, the issues of achieving import independence in providing Russian industry with strategic metals are considered. The problems of prospecting and exploration of ores of such metals, their enrichment and extraction are discussed. Solutions to some of the identified problems proposed by the Russian Academy of Sciences are presented. Considerable attention is paid to the mining and extraction of lithium and rare earth elements.The article is based on a report at the scientific session of the General Meeting of Members of the Russian Academy of Sciences on December 12, 2023, using materials from a joint meeting of the Scientific Council of the Russian Academy of Sciences on Materials and Nanomaterials, the Interdepartmental Scientific Council of the Russian Academy of Sciences on the development of the mineral resource base and its rational use, the bureau of the Department of Chemistry and Material Sciences of the Russian Academy of Sciences, the Bureau of the Department of Geosciences of the Russian Academy of Sciences and with the participation of representatives of the State Corporation “Rosatom”, the Ministry of Industry and Trade of Russia and the Ministry of Education and Science of Russia.

N. Kshetri

PurposeThe purpose of this paper is to examine blockchain's roles in promoting ethical sourcing in the mineral and metal industry.Design/methodology/approachIt analyzes multiple case studies of blockchain projects in the mineral and metal industry.FindingsIt gives detailed descriptions of how blockchain-based supply chain networks' higher density of information flow and high degree of authenticity of information can increase supply chain participants' compliance with sustainability standards. It gives special consideration to blockchain systems' roles in overcoming the deficits in the second party and the third-party trust. It also demonstrates how blockchain-based supply chain networks include outside actors and configure the supply chain networks in a way that enhances the empowerment of marginalized groups.Practical implicationsIt suggests various mechanisms by which blockchain-based supply chain networks can give a voice to marginalized groups.Originality/valueIt demonstrates how blockchain is likely to force mineral and metal supply chains to become more traceable and transparent.

Clashon Onesmo, E. Mabhuye, P. Ndaki

The increased demand for secondary materials, particularly scrap metals, in cities due to development activities in both emerging economies and developing countries has increased the demand for recycling materials. It accelerated the growth of the circular economy and climate-smart development. This paper investigated the synergy between sustainable solid waste management and the circular economy in Tanzanian cities by examining the scrap business’s categories, quantity, market, and nature and the scrap business’s environmental benefits in Arusha. The study found that iron steel, cast iron, and aluminum were the most common scrap metal recovered and traded in Arusha. Offices and institutions, households, and garages were the primary sources of scraps. Over 314 tonnes of scrap metal were traded monthly in the city. The scrap business helped the steel industries save 300 tons of iron ore, 164 tons of coal, and 64 tons of bauxite while lowering their monthly energy consumption by 56%. Scrap metal trade contributes significantly to recycling, climate-smart, circular economy, and improving livelihoods. As a result, we call for a synchronized sustainable development and solid waste management system that connects product design, development manufacturing, and end-of-life products to improve the circular economy.

Jiangbo Ma, Xiaotong Wu, Xiansheng Yan

A large number of Yue style bronzes with regional cultural characteristics were unearthed in Hunan, which is of great significance for studying the cross-regional circulation of bronze technology and metal resources in the south of the Yangtze River during the Late Bronze Age (8 ~ 5 C. BCE) in China. In this study, 30 Yue style bronzes and 3 Chu style bronzes unearthed from five regions in Hunan Province were analyzed for chemical composition, metallography and lead isotopes. The results show that the alloy materials of Hunan Yue style bronze ware are diverse. The containers are mainly leaded tin bronze, with both tin bronze and copper. The weapons or tools are mostly tin bronze, and the alloy composition is primarily tin. The lead isotope ratio analysis results showed three main ore sources: polymetallic deposits in the Nanling Mountains, the eastern Hubei-northern Jiangxi metallogenic belt and the western Henan Qinling-Dabie metallogenic belt. The extensive source of minerals reflects the frequent trade of metal resources between Yue people and the Chu state, which is not only the economic basis for the close relationship between Hunan Yue people and Chu State but also an important driving force for the southward expansion of the Chu state and national integration in Hunan.

Lucas Woodley, Chung Yi See, Peter Cook et al.

Recently proposed tailpipe emissions standards aim to significant increases in electric vehicle (EV) sales in the United States. Our work examines whether this increase is achievable given potential constraints in EV mineral supply chains. We estimate a model that reflects international sourcing rules, heterogeneity in the mineral intensity of predominant battery chemistries, and long-run grid decarbonization efforts. Our efforts yield five key findings. First, compliance with the proposed standard necessitates replacing at least 10.21 million new ICEVs with EVs between 2027 and 2032. Second, based on economically viable and geologically available mineral reserves, manufacturing sufficient EVs is plausible across most battery chemistries and could, subject to the chemistry leveraged, reduce up to 457.3 million total tons of CO2e. Third, mineral production capacities of the US and its allies constrain battery production to a total of 5.09 million EV batteries between 2027 and 2032, well short of deployment requirements to meet EPA standards even if battery manufacturing is optimized to exclusively manufacture materials efficient NMC 811 batteries. Fourth, disequilibrium between mineral supply and demand results in at least 59.54 million tons of CO2e in total lost lifecycle emissions benefits. Fifth, limited present-day production of battery-grade graphite and to a lesser extent, cobalt, constrain US electric vehicle battery pack manufacturing under strict sourcing rules. We demonstrate that should mineral supply bottlenecks persist, hybrid electric vehicles may offer equivalent lifecycle emissions benefits as EVs while relaxing mineral production demands, though this represents a tradeoff of long-term momentum in electric vehicle deployment in favor of near-term carbon dioxide emissions reductions.

Đukić Miloš, Vasiljević Sanela, Sovrlić Zorica et al.

Validation of analytical methods ensures reliability and accuracy of analytical data. Accredited laboratories in accordance with the international standard ISO/IEC 17025, that is the Serbian standard SRPS ISO/IEC 17025, must fulfill and document the selection, validation, or verification of the analytical method. In the validation and verification process, it is mandatory to assess: limit of detection (LoD), limit of quantification (LoQ), linearity and measurement uncertainty.