Hasil untuk "Mineral industries. Metal trade"

Fei Meng, James McNeice, S. S. Zadeh et al.

ABSTRACT Rechargeable lithium-ion batteries (LIBs) are widely employed in portable electric devices, electric vehicles (EVs), and hybrid electric vehicles (HEVs), indicating a potential increasing demand for LIBs over the next decade. Lithium, a critical element in LIBs, might encounter a potential supply crisis in the future. The irregular distribution of lithium mineral resources in countries and the unequal concentration in brine reserves also causes lithium extraction to be of critical importance. Today lithium is mainly recovered from minerals (especially spodumene) by acid, alkaline, and chlorination processes, and from brines by crystallization, solvent extraction, and ion-exchange processes. Regarding the secondary resources, i.e., recycling the spent LIBs, the recycling process consists of dismantling the LIBs, in some cases the separation of the cathode and anode materials, leaching of shredded material, and separation and recovery of metals. Nonetheless, the industry standard for recycling of LIBs currently is the pyrometallurgy processes, mostly are focused on the base metals recovery, such as cobalt and nickel, rather than lithium. Varying compositions of batteries for different applications require the development of a suitable and sustainable recycling process to recover metals from all types of LIBs. This paper provides a comprehensive review of lithium recovery processes that have already been studied and are currently in industrial practice, in the hope of providing some inspirations to explore new technologies for sustainable recovery of lithium from minerals, brines and LIBs.

H. Wagner

Increasing demand for metals caused by global economic growth and exploitation of shallow mineral deposits forces mineral extraction to go deeper. A direct consequence of this development is an increase in rock pressure-related mining problems. The role of rock engineering in the design and operation of deep mines is discussed in detail. Critical issues are the rock fracturing around mining excavations, the support and control of the fractured rock, and the rock mechanics design of mine infrastructure and extraction (stoping) systems. Progress of the science of rock mechanics in the areas related to these issues is highlighted and critically examined. Specific areas are the prediction and assessment of the mechanical properties of rock mass, the mechanics of controlling fractured rock around deep mining excavations and the resulting demands on support systems. Rock engineering aspects of stoping systems and the regional stress changes resulting from the extraction of large mineral bodies are discussed in detail. The progress in design concepts for open stopes and stopes with caving of the roof strata is illustrated. It is shown that the stress environment in deep mines does not favour the highly productive caving systems of stoping. The value of energy-based design concepts for very deep mines exploiting tabular mineral deposits is shown. Despite the considerable progress that has been made in the science of rock mechanics since the 1950s, progress in applying this knowledge to solve rock pressure problems in deep mines has been rather slow. The tools are available. What is needed is the development of robust design criteria for mine infrastructure, excavations and support systems for dynamic and changing stress environments. The second critical issue is the lack of highly qualified rock engineering personnel on the mines. This has been recognized by the European mining industry through supporting a continued education programme in rock engineering for deep mines.

Jurek Preker

We introduce a novel equilibrium concept that incorporates Knightian uncertainty into the cursed equilibrium (Eyster and Rabin, 2005). This concept is then applied to a two-player game in which agents can engage in trade or refuse to do so. While the Bayesian Nash equilibrium predicts that trade never happens, players do trade in a cursed equilibrium. The inclusion of uncertainty enhances this effect for cursed and uncertainty averse players. This contrasts general findings that uncertainty reduces trade but is consistent with behavior that has been observed in experiments.

Zahid Ullah, A. Rashid, Junaid Ghani et al.

Groundwater contamination through potentially harmful metals (PHMs) is an environmental hazard in Pakistan with significant human health risk reports. The current research was conducted in Sheikhupura District, which is a major industrial site in Punjab, Pakistan. According to the Punjab Directorate of Industries in Pakistan, there are a total of 748 industries in this area. These industries produce a lot of waste and effluent, which contaminate the environment with harmful and toxic materials. Continuous irrigation with industrial effluent and sewage sludge may make groundwater sources vulnerable. Therefore, we collected 243 groundwater samples from community tube wells to investigate the groundwater quality cconcerning PHM contaminations in the study area. This research presents the values of pH, total dissolved solids (TDS), electrical conductivity (EC), and potentially harmful metals (PHMs) like arsenic (As), manganese (Mn), lead (Pb), zinc (Zn), copper (Cu), nickel (Ni), and iron (Fe). PHMs such as As (91%), Mn (14%), Pb (97%), Fe (45%), Zn (15%), in these samples were beyond the permitted limit recommended by the world health organization (WHO). Principal component analysis (PCA) results with total variability of (60%) reveal that the groundwater sources of the study area are contaminated about 30.9, 31.3, and 37.6% of contaminations of groundwater sources of this study are resulted from geogenic sources, anthropogenic sources, or both geogenic and anthropogenic sources, respectively. Such sources may include rock-water interaction, mining actions, agricultural practices, domestic sewage, and industrial effluent in the study area. Saturation indices show that the aquifers of the study area are saturated with lead hydroxide, zinc hydroxide, and goethite minerals, indicating that these minerals have a vital role in the contamination of groundwater. Health risk assessment results predicted that the non-carcinogenic risk (HQ) values of PHMs were found within the permissible limit (<1), except As (1.58E+00) for children, while carcinogenic risk (CR) values of all selected PHMs were lower than the maximum threshold CR value (1 × 10−4).

Benjamin Cox, S. Innis, N. Kunz et al.

The technology used in renewable energy production is resulting in a material increase in the demand for many minerals and metals. While the mining industry contributes to global carbon dioxide emissions, the industry is also critical to lowering global carbon emissions across the broader economy. Here we test the impact of a hypothetical international carbon taxation regime on a subsection of the mining industry compared to other sectors. A financial model was developed to calculate the cost of carbon taxes for 23 commodities across three industries. The findings show that, given any level of taxation tested, most mining industry commodities would not add more than 30% of their present product value. Comparatively, commodities such as coal could be taxed at more than 150% of their current product value under more intense carbon pricing initiatives, thereby accelerating the transition to renewable energy sources and the consequent demand benefits for mined metals. Global carbon taxation would provide a net economic benefit to the mining industry by raising demand in metals and minerals, in contrast to more energy-intensive industries for replaceable commodities, suggests a financial model analysis of the carbon tax costs for various sectors.

Yunhua Fu, Renbiao Tao, Lifei Zhang et al.

Nominally anhydrous minerals (NAMs) composing Earth's and planetary rocks incorporate microscopic amounts of volatiles. However, volatile distribution in NAMs and their effect on physical properties of rocks remain controversial. Thus, constraining trace volatile concentrations in NAMs is tantamount to our understanding of the evolution of rocky planets and planetesimals. Here, we present a novel approach of trace-element quantification using micro-scale Nuclear Magnetic Resonance (NMR) spectroscopy. This approach employs the principle of enhanced mass-sensitivity in NMR microcoils formerly used in \textit{in-situ} high pressure experiments. We were able to demonstrate that this method is in excellent agreement with standard methods across their respective detection capabilities. We show that by simultaneous detection of internal reference nuclei, the quantification sensitivity can be substantially increased, leading to quantifiable trace volatile element amounts of about $50$ wt-ppb measured in a micro-meter sized single anorthitic mineral grain, greatly enhancing detection capabilities of volatiles in geologically important systems.

Juan Sosa, Andrés Felipe Arévalo-Arévalo, Juan Pablo Torres-Clavijo

Globalization has rapidly advanced but exposed countries to supply chain disruptions, highlighted by the COVID-19 pandemic. This study exhaustively analyzes bilateral export data for 186 countries from 2018, 2020, and 2022, using Exponential Random Graph Models (ERGMs), to identify determinants of trade relationships, as well as Stochastic Block Models (SBMs), to characterize countries' roles in the trade network. Our findings show persistent, significant nodal characteristics driving bilateral trade and reveal no major structural changes in the trade network due to the pandemic.

Xiao Wang, Yutong Wang, Jing Yang et al.

As the concept of Industries 5.0 develops, industrial metaverses are expected to operate in parallel with the actual industrial processes to offer ``Human-Centric" Safe, Secure, Sustainable, Sensitive, Service, and Smartness ``6S" manufacturing solutions. Industrial metaverses not only visualize the process of productivity in a dynamic and evolutional way, but also provide an immersive laboratory experimental environment for optimizing and remodeling the process. Besides, the customized user needs that are hidden in social media data can be discovered by social computing technologies, which introduces an input channel for building the whole social manufacturing process including industrial metaverses. This makes the fusion of multi-source data cross Cyber-Physical-Social Systems (CPSS) the foundational and key challenge. This work firstly proposes a multi-source-data-fusion-driven operational architecture for industrial metaverses on the basis of conducting a comprehensive literature review on the state-of-the-art multi-source data fusion methods. The advantages and disadvantages of each type of method are analyzed by considering the fusion mechanisms and application scenarios. Especially, we combine the strengths of deep learning and knowledge graphs in scalability and parallel computation to enable our proposed framework the ability of prescriptive optimization and evolution. This integration can address the shortcomings of deep learning in terms of explainability and fact fabrication, as well as overcoming the incompleteness and the challenges of construction and maintenance inherent in knowledge graphs. The effectiveness of the proposed architecture is validated through a parallel weaving case study. In the end, we discuss the challenges and future directions of multi-source data fusion cross CPSS for industrial metaverses and social manufacturing in Industries 5.0.

Md Bokhtiar Al Zami, Shaba Shaon, Vu Khanh Quy et al.

Industrial networks are undergoing rapid transformation driven by the convergence of emerging technologies that are revolutionizing conventional workflows, enhancing operational efficiency, and fundamentally redefining the industrial landscape across diverse sectors. Amidst this revolution, Digital Twin (DT) emerges as a transformative innovation that seamlessly integrates real-world systems with their virtual counterparts, bridging the physical and digital realms. In this article, we present a comprehensive survey of the emerging DT-enabled services and applications across industries, beginning with an overview of DT fundamentals and its components to a discussion of key enabling technologies for DT. Different from literature works, we investigate and analyze the capabilities of DT across a wide range of industrial services, including data sharing, data offloading, integrated sensing and communication, content caching, resource allocation, wireless networking, and metaverse. In particular, we present an in-depth technical discussion of the roles of DT in industrial applications across various domains, including manufacturing, healthcare, transportation, energy, agriculture, space, oil and gas, as well as robotics. Throughout the technical analysis, we delve into real-time data communications between physical and virtual platforms to enable industrial DT networking. Subsequently, we extensively explore and analyze a wide range of major privacy and security issues in DT-based industry. Taxonomy tables and the key research findings from the survey are also given, emphasizing important insights into the significance of DT in industries. Finally, we point out future research directions to spur further research in this promising area.

S. Mohammadi-Jam, Ziyi Li, N. Rose et al.

The mining industry aims to promote responsible chemical use during mineral processing operations to minimize the chemical contamination. Hydroxamic acids, which can form strong chelates with metals, have been shown to have less health and environmental issues when compared to xanthate collectors. In this work, the performance of benzohydroxamic acid (BHA) as a collector for galena, chalcopyrite, and quartz was evaluated. The minerals were conditioned with different concentrations (1.5, 3, and 4.5 kg/t) of collector at pHs 8, 9, and 10. The result showed that the treatment of the mineral surfaces with BHA enhanced the flotation recoveries of the sulfide minerals. High concentrations of benzohydroxamate anion, the protonic dissociation product of BHA, existed at basic pHs, where a chemical reaction between the anion and a metal cation on the mineral surface resulted in the adsorption of the collector onto the mineral surface. The microflotation results showed that the BHA collector was able to successfully recover galena and chalcopyrite. Their flotation recovery was dependent on the conditioning pH. Galena showed a high flotation recovery (up to 86%) at both pH 9 and 10, whereas chalcopyrite became most hydrophobic at pH values of 8 and 9 (up to 88%). None of the BHA concentrations or conditioning pHs was able to enhance quartz recovery beyond 7%. The research results have implications in the application of BHA for the froth flotation of galena and chalcopyrite.

Jay Lee, Hanqi Su

The recent emergence of large language models (LLMs) demonstrates the potential for artificial general intelligence, revealing new opportunities in Industry 4.0 and smart manufacturing. However, a notable gap exists in applying these LLMs in industry, primarily due to their training on general knowledge rather than domain-specific knowledge. Such specialized domain knowledge is vital for effectively addressing the complex needs of industrial applications. To bridge this gap, this paper proposes a unified industrial large knowledge model (ILKM) framework, emphasizing its potential to revolutionize future industries. In addition, ILKMs and LLMs are compared from eight perspectives. Finally, the "6S Principle" is proposed as the guideline for ILKM development, and several potential opportunities are highlighted for ILKM deployment in Industry 4.0 and smart manufacturing.

Maksim Papenkov, Chris Meredith, Claire Noel et al.

Accurate industry classification is a critical tool for many asset management applications. While the current industry gold-standard GICS (Global Industry Classification Standard) has proven to be reliable and robust in many settings, it has limitations that cannot be ignored. Fundamentally, GICS is a single-industry model, in which every firm is assigned to exactly one group - regardless of how diversified that firm may be. This approach breaks down for large conglomerates like Amazon, which have risk exposure spread out across multiple sectors. We attempt to overcome these limitations by developing MIS (Multi-Industry Simplex), a probabilistic model that can flexibly assign a firm to as many industries as can be supported by the data. In particular, we utilize topic modeling, an natural language processing approach that utilizes business descriptions to extract and identify corresponding industries. Each identified industry comes with a relevance probability, allowing for high interpretability and easy auditing, circumventing the black-box nature of alternative machine learning approaches. We describe this model in detail and provide two use-cases that are relevant to asset management - thematic portfolios and nearest neighbor identification. While our approach has limitations of its own, we demonstrate the viability of probabilistic industry classification and hope to inspire future research in this field.

Donzhyun Park, Shuzhi Liu

Purpose – This study addresses the development of China’s semiconductor industry in the context of the U.S.-China trade conflict, and analyzes the impact on other industries. Design/Methodology/Approach – Based on the multi-regional input-output table industry splitting method, the electrical and electronic equipment manufacturing industry in the Asian Development Bank’s multi-regional input-output table (ADB-MRIO, 2019) is split into semiconductor and non-semiconductor industries, and the impact of U.S. export controls on China’s semiconductor exports on domestic and foreign economies is simulated and analyzed using the hypothesis extraction and hypothesis expansion methods. Findings – The United States has suffered more than China from US export controls on semiconductors to China, and the impact of U.S. export controls on U.S. GDP decreasing by at most 0.0124‰, and China’s GDP decreasing by at most 0.00089‰. Since Japan, Korea, and European countries have become China’s semiconductor import substitutes, they all benefit from U.S. export controls on China. Second, the most affected industries in China are the chemical products, metal products, wholesale, financial, and non-semiconductor industries in the electrical and electronic equipment manufacturing industry. Research Implications – China should adopt coping strategies such as deepening international exchanges, enhancing communication between China and the U.S., and strengthening its scientific and technological strength.

J. Hayes, Alem Cherinet

Demand for critical minerals, battery metals, and the nearshoring of electric vehicle (EV) manufacturing have implications for all trading partners of the updated North American Free Trade Agreement, now called the United States-Mexico-Canada Agreement (USMCA). North American EV manufacturing is driven by initiatives such as the battery belt in the US and Canada’s commitment to clean technology. Mexico, as a major auto-component manufacturer and producer of critical minerals, holds a significant role in supporting the regional supply chain. However, recent developments in Mexican natural resource policy, including the nationalization of lithium deposits and exploration moratoriums, present challenges for foreign miners operating in Mexico, including the risk of future limited participation in the mining sector. Canadian miners hold a dominant role in Mexican mineral exploration, and Mexico is Canada’s third-largest trading partner. The political landscape in Mexico, with the ruling Morena party controlling both the national government and majority of state governments, further complicates the situation. Policy changes in 2023 to the mining sector’s regulatory requirements are the most significant reforms to the sector since the early 1990s. The reforms are in response to prominent, long-standing grievances from various non-industry stakeholders and seek to mitigate against future negative social and environmental impacts of mining. The reforms include shorter mining concession permits, stricter environmental impact assessments, and new permitting procedures on water use.

Dong-joo Lee, Jeongho Yoo

Purpose - The Effect of EU foreign trade policy on global supply chains is growing. The EU’s Carbon Border Adjustment Mechanism (CBAM), which is deeply involved in international trade, is expected to directly or indirectly affect the global supply chain. Although the discussion of CBAM is spreading, it is difficult to find a study analyzing the negative effect of CBAM on the supply chain. Therefore, this study aims to analyze the effect of the introduction of the CBAM on Korean exports. Design/Methodology/Approach - For the analysis method, we use the GTAP model, which is a Computable General Equilibrium (CGE) model, and in particular, we use the GTAP-E model, a model specialized for environmental policies such as climate change. And for the database required for the CGE model, we use the GTAP-E database specialized for the GTAP-E model. Findings - As a result of the analysis, it was analyzed that Korea’s output and exports decreased. In particular, the decrease in output in the chemicals, metal, and machinery industries was the largest, and Korea’s exports to China decreased by up to 322.7 million dollars in the chemical industry. As a means to overcome the introduction of CBAM, we analyzed the effects of support for renewable energy, introduction of low-carbon technology, and tax benefits. As a result of the analysis, the effect of expanding renewable energy support was limited, while the effect of introducing low-carbon technology and tax benefits was analyzed to expand exports by up to 2.6% and 4.86%, respectively. Research Implications - From the analysis results of this study, it can be seen that the climate change response policies, which were introduced in a voluntary way by each country, spread in an involuntary way through the CBAM. Although there is a possibility that environmental measures may not conform to the WTO agreement, it is necessary for both the government and firms to actively participate in environmental issues as carbon-related measures are likely to be actively discussed in the international trade environment.

B. Farida

India has always been rich in its natural resources and its products. All these rich resources offered scope for a large number of industries, handicrafts and many other professions [I]. Likewise, industrial works and other economic professions were encouraged during the Gupta period also since raw materials were abundant for industries. Gemstone is a piece of mineral crystal that in cut and polished form is used to make jewellery or other adornments. One of the most ancient Industries of India was the precious stone Industry which includes diamonds, pearls, ruby and emerald among many others. The Guptas were also skilled in craftsmanship. They got the credit for organizing the technical skill of the craftsmen under royal supervision and inducing them to be enterprising in their trade. They inherited the fine craftsmanship of the Indians from an earlier period [II]. The science of testing gems was so perfected in India in this period as to arouse the admiration of the later European travellers of the 16th century. In the Kamasutra ruparatnapariksha (testing and valuing of precious stones etc.) are included in the list of sixty-four arts [III]. The jewellers [IV] used scales and touchstones for weighing and testing the quality and quantity of stones and metals. Such a high level of culture and the enhanced standard of living of the rich were impossible without a well developed urbanization and trade network. It is a fact from the Gupta period, trade had started falling on bad days but localized economic formations started taking place. By the time of this period, the trade become luxury oriented, a fact attested by the frequency of references to such items in the Brihatsamhita and many other ancient Sanskrit literature [V].

Célestin Coquidé, José Lages, Leonardo Ermann et al.

Using the United Nations Comtrade database, we perform the Google matrix analysis of the multiproduct World Trade Network (WTN) for the years 2018-2020 comprising the emergence of the COVID-19 as a global pandemic. The applied algorithms -- the PageRank, the CheiRank and the reduced Google matrix -- take into account the multiplicity of the WTN links providing new insights on the international trade comparing to the usual import-export analysis. These algorithms establish new rankings and trade balances of countries and products considering every countries on equal grounds, independently of their wealth, and every products on the basis of their relative exchanged volumes. In comparison with the pre-COVID-19 period, significant changes in these metrics occur for the year 2020 highlighting a major rewiring of the international trade flows induced by the COVID-19 pandemic crisis. We define a new PageRank-CheiRank product trade balance, either export or import oriented, which is significantly perturbed by the pandemic.

Sae Young Moon, Gregor Kerr, Fran Silavong et al.

When designing a new API for a large project, developers need to make smart design choices so that their code base can grow sustainably. To ensure that new API components are well designed, developers can learn from existing API components. However, the lack of standardized methods for comparing API designs makes this learning process time-consuming and difficult. To address this gap we developed API-Miner, to the best of our knowledge, one of the first API-to-API specification recommendation engines. API-Miner retrieves relevant specification components written in OpenAPI (a widely adopted language used to describe web APIs). API-miner presents several significant contributions, including: (1) novel methods of processing and extracting key information from OpenAPI specifications, (2) innovative feature extraction techniques that are optimized for the highly technical API specification domain, and (3) a novel log-linear probabilistic model that combines multiple signals to retrieve relevant and high quality OpenAPI specification components given a query specification. We evaluate API-Miner in both quantitative and qualitative tasks and achieve an overall of 91.7% recall@1 and 56.2% F1, which surpasses baseline performance by 15.4% in recall@1 and 3.2% in F1. Overall, API-Miner will allow developers to retrieve relevant OpenAPI specification components from a public or internal database in the early stages of the API development cycle, so that they can learn from existing established examples and potentially identify redundancies in their work. It provides the guidance developers need to accelerate development process and contribute thoughtfully designed APIs that promote code maintainability and quality. Code is available on GitHub at https://github.com/jpmorganchase/api-miner.

Tingting Cao, Weiqing Sun, Cuiping Sun et al.

Ferrous metal futures have become unique commodity futures with Chinese characteristics. Due to the late listing time, it has received less attention from scholars. Our research focuses on the volatility spillover effects, defined as the intensity of price volatility in financial instruments. We use DCC-GARCH, BEKK-GARCH, and DY(2012) index methods to conduct empirical tests on the volatility spillover effects of the Chinese ferrous metal futures market and other parts of the Chinese commodity futures market, as well as industries related to the steel industry chain in stock markets. It can be seen that there is a close volatility spillover relationship between ferrous metal futures and nonferrous metal futures. Energy futures and chemical futures have a significant transmission effect on the fluctuations of ferrous metals. In addition, ferrous metal futures have a significant spillover effect on the stock index of the steel industry, real estate industry, building materials industry, machinery equipment industry, and household appliance industry. Studying the volatility spillover effect of the ferrous metal futures market can reveal the operating laws of this field and provide ideas and theoretical references for investors to hedge their risks. It shows that the ferrous metal futures market has an essential role as a "barometer" for the Chinese commodity futures market and the stock market.

Xiaohang Zhao, Xiao Fang, Jing He et al.

Industry assignment, which assigns firms to industries according to a predefined Industry Classification System (ICS), is fundamental to a large number of critical business practices, ranging from operations and strategic decision making by firms to economic analyses by government agencies. Three types of expert knowledge are essential to effective industry assignment: definition-based knowledge (i.e., expert definitions of each industry), structure-based knowledge (i.e., structural relationships among industries as specified in an ICS), and assignment-based knowledge (i.e., prior firm-industry assignments performed by domain experts). Existing industry assignment methods utilize only assignment-based knowledge to learn a model that classifies unassigned firms to industries, and overlook definition-based and structure-based knowledge. Moreover, these methods only consider which industry a firm has been assigned to, but ignore the time-specificity of assignment-based knowledge, i.e., when the assignment occurs. To address the limitations of existing methods, we propose a novel deep learning-based method that not only seamlessly integrates the three types of knowledge for industry assignment but also takes the time-specificity of assignment-based knowledge into account. Methodologically, our method features two innovations: dynamic industry representation and hierarchical assignment. The former represents an industry as a sequence of time-specific vectors by integrating the three types of knowledge through our proposed temporal and spatial aggregation mechanisms. The latter takes industry and firm representations as inputs, computes the probability of assigning a firm to different industries, and assigns the firm to the industry with the highest probability.