Hasil untuk "Mining engineering. Metallurgy"

Yang Lv, Si-Wen Ke, Yuming Gu et al.

Nitrate-containing industrial wastewater poses a serious threat to the global food security and public health safety. As compared to the traditional microbial denitrification, electrocatalytic nitrate reduction shows better sustainability with ultrahigh energy efficiency and the production of high-value ammonia (NH3). However, nitrate-containing wastewater from most industrial processes, such as mining, metallurgy, and petrochemical engineering, is generally acidic, which contradicts the typical neutral/alkaline working conditions for both denitrifying bacteria and the state-of-the-art inorganic electrocatalysts, leading to the demand for pre-neutralization and the problematic hydrogen evaluation reaction (HER) competition and catalyst dissolution. Here, we report a series of FeM2 (M = Fe, Co, Ni, Zn) trinuclear cluster metal-organic frameworks (MOFs) that enable the highly efficient electrocatalytic nitrate reduction to ammonium under strong acidic conditions with excellent stability. In pH=1 electrolyte, the Fe2Co-MOF demonstrates the NH3 yield rate of 20653.5 μg·h-1·mg-1site with 90.55% NH3-Faradaic efficiency (FE), 98.5% NH3-selectivity and up to 75 hr of electrocatalytic stability. Additionally, successful nitrate reduction in high-acidic conditions directly produce the ammonium sulfate as nitrogen fertilizer, avoiding the subsequent aqueous ammonia extraction and preventing the ammonia spillage loss. This cluster-based MOF structure provide new insights into the design principles of high-performance nitrate reduction catalysts under environmentally-relevant wastewater conditions.

G. Panagiotou, J. Sturgul

The symposium is organized by the Department of Mining Engineering and Metallurgy of the National University of Athens and the Department of Metallurgy and Mining of the University of Idaho. It is intended that this symposium will be the first in a series of symposia on system simulation, artificial intelligence techniques, virtual reality and other related topics, as they apply in mining. The complete proceedings are published on CD-ROM with an accompanying book which contains abstracts (including full title, authors' names and e-mail addresses as well as a keyword index) of all the papers. Also included are the full texts of keynote lectures and a short introduction about the experience gained with this new way of holding a conference.

Klara Borowa, Andrzej Zalewski, Lech Madeyski

The software engineering researchers from countries with smaller economies, particularly non-English speaking ones, represent valuable minorities within the software engineering community. As researchers from Poland, we represent such a country. We analyzed the ICSE FOSE (Future of Software Engineering) community survey through reflexive thematic analysis to show our viewpoint on key software community issues. We believe that the main problem is the growing research-industry gap, which particularly impacts smaller communities and small local companies. Based on this analysis and our experiences, we present a set of recommendations for improvements that would enhance software engineering research and industrial collaborations in smaller economies.

Dectot--Le Monnier de Gouville Esteban, Mohammad Hamdaqa, Moataz Chouchen

YARA has established itself as the de facto standard for "Detection as Code," enabling analysts and DevSecOps practitioners to define signatures for malware identification across the software supply chain. Despite its pervasive use, the open-source YARA ecosystem remains characterized by ad-hoc sharing and opaque quality. Practitioners currently rely on public repositories without empirical evidence regarding the ecosystem's structural characteristics, maintenance and diffusion dynamics, or operational reliability. We conducted a large-scale mixed-method study of 8.4 million rules mined from 1,853 GitHub repositories. Our pipeline integrates repository mining to map supply chain dynamics, static analysis to assess syntactic quality, and dynamic benchmarking against 4,026 malware and 2,000 goodware samples to measure operational effectiveness. We reveal a highly centralized structure where 10 authors drive 80% of rule adoption. The ecosystem functions as a "static supply chain": repositories show a median inactivity of 782 days and a median technical lag of 4.2 years. While static quality scores appear high (mean = 99.4/100), operational benchmarking uncovers significant noise (false positives) and low recall. Furthermore, coverage is heavily biased toward legacy threats (Ransomware), leaving modern initial access vectors (Loaders, Stealers) severely underrepresented. These findings expose a systemic "double penalty": defenders incur high performance overhead for decayed intelligence. We argue that public repositories function as raw data dumps rather than curated feeds, necessitating a paradigm shift from ad-hoc collection to rigorous rule engineering. We release our dataset and pipeline to support future data-driven curation tools.

Karina Kohl, Luigi Carro

Software Engineering (SE) faces simultaneous pressure from AI automation (reducing code production costs) and hardware-energy constraints (amplifying failure costs). We position that SE must redefine itself around human discernment-intent articulation, architectural control, and verification-rather than code construction. This shift introduces accountability collapse as a central risk and requires fundamental changes to research priorities, educational curricula, and industrial practices. We argue that Software Engineering, as traditionally defined around code construction and process management, is no longer sufficient. Instead, the discipline must be redefined around intent articulation, architectural control, and systematic verification. This redefinition shifts Software Engineering from a production-oriented field to one centered on human judgment under automation, with profound implications for research, practice, and education.

Murali Pujari, B. Shingan, A. Arya et al.

Membrane‐based separation processes have been gaining significant attention in the treatment of oily wastewater. To date, a remarkable amount of data is available on the application of membranes in diverse domains such as industrial wastewater treatment, food processing, and medicine. It is becoming a severe issue when process sectors like mining, metallurgy, and petrochemicals discharge oily wastewater. Although oil–water emulsion separation using membrane technology is successful, this method suffers from a serious flux declination problem brought on by fouling during filtration. Keeping this in mind, the aim of this paper is to highlight the recent advancements in the synthesis of ceramic membranes from several perspectives such as feed pretreatment, membrane cleaning, proper operational conditions, and the use of antifouling coatings. Recent study has indicated that surface hydrophilization is the key emphasis in mitigating membrane fouling. Thus, the current state of membrane surface modification technology is reviewed, and future trends are identified.

Huaigang Cheng, Jialu Wang, Huiping Song et al.

Ashis Kumar Mandal, Md Nadim, Chanchal K. Roy et al.

Research in software engineering is essential for improving development practices, leading to reliable and secure software. Leveraging the principles of quantum physics, quantum computing has emerged as a new computational paradigm that offers significant advantages over classical computing. As quantum computing progresses rapidly, its potential applications across various fields are becoming apparent. In software engineering, many tasks involve complex computations where quantum computers can greatly speed up the development process, leading to faster and more efficient solutions. With the growing use of quantum-based applications in different fields, quantum software engineering (QSE) has emerged as a discipline focused on designing, developing, and optimizing quantum software for diverse applications. This paper aims to review the role of quantum computing in software engineering research and the latest developments in QSE. To our knowledge, this is the first comprehensive review on this topic. We begin by introducing quantum computing, exploring its fundamental concepts, and discussing its potential applications in software engineering. We also examine various QSE techniques that expedite software development. Finally, we discuss the opportunities and challenges in quantum-driven software engineering and QSE. Our study reveals that quantum machine learning (QML) and quantum optimization have substantial potential to address classical software engineering tasks, though this area is still limited. Current QSE tools and techniques lack robustness and maturity, indicating a need for more focus. One of the main challenges is that quantum computing has yet to reach its full potential.

Tim Wittenborg, Ildar Baimuratov, Ludvig Knöös Franzén et al.

The aerospace industry operates at the frontier of technological innovation while maintaining high standards regarding safety and reliability. In this environment, with an enormous potential for re-use and adaptation of existing solutions and methods, Knowledge-Based Engineering (KBE) has been applied for decades. The objective of this study is to identify and examine state-of-the-art knowledge management practices in the field of aerospace engineering. Our contributions include: 1) A SWARM-SLR of over 1,000 articles with qualitative analysis of 164 selected articles, supported by two aerospace engineering domain expert surveys. 2) A knowledge graph of over 700 knowledge-based aerospace engineering processes, software, and data, formalized in the interoperable Web Ontology Language (OWL) and mapped to Wikidata entries where possible. The knowledge graph is represented on the Open Research Knowledge Graph (ORKG), and an aerospace Wikibase, for reuse and continuation of structuring aerospace engineering knowledge exchange. 3) Our resulting intermediate and final artifacts of the knowledge synthesis, available as a Zenodo dataset. This review sets a precedent for structured, semantic-based approaches to managing aerospace engineering knowledge. By advancing these principles, research, and industry can achieve more efficient design processes, enhanced collaboration, and a stronger commitment to sustainable aviation.

Ildar M. Ishkulov, Irik G. Fattakhov

The problem of timely and accurate evaluation of well integrity is becoming increasingly relevant in the context of mature field development, high wellstream water cut, and a growing number of old wells. For production casing diagnostics, geophysical methods are typically used to identify damage and determine its interval. However, high workload of field personnel hinders prompt deployment of wireline crews to survey the integrity of wells. This results in lost oil production, increased water cut, environmental risks, increased non-productive injected volumes, and reduced key economic indices. To address these challenges, a novel approach to evaluation of casing string integrity based on machine learning models has been proposed. The paper presents a procedure for application of interpretable machine learning to detect production casing leakage and provides a comparison of this approach with the ROC-AUC statistical analysis method. The novel approach integrates the LightGBM machine learning algorithm and SHAP analysis to evaluate contribution of key features to well integrity prediction and determine their threshold values. The model training was based on data from 14,318 well surveys conducted between 2000 and 2022. The results indicate that the most important features are sulfate content, solution supersaturation ratio, and water cut. The study confirms the efficiency of interpretable machine learning methods for diagnosing complex technical systems. These results show the potential for application of such models in well integrity monitoring and well workover planning. This approach can also be used in other oil and gas applications, such as prediction of various problems and optimization of well operation conditions.

HU Minglei, HU Bin, ZHANG Wei et al.

Specimen reconstitution technology is important for the in-service safety of reactor pressure vessels (RPVs) in nuclear power plants. To verify the feasibility of electron beam welding for the restructured specimens, the electron beam welding to restructure the 16MND5, 10Cr12Ni3Mo2VN, and X6NiCrTiMoVB 25-15-2 Charpy impact specimens for three kinds of steels was adopted, the microstructure and mechanical properties of the restructured specimens were characterized, and the Vickers hardness and impact toughness of the specimens were tested by non-destructive flaw detection, optical microscopy, and scanning electron microscopy. The results show that the microstructure of these three steels in weld zone is relatively dense, and no obvious crack defects occur. The welding quality of 10Cr12Ni3Mo2VN restructured specimens is the best, and the mechanical properties are relatively close to those of the original specimens, demonstrating that the electron beam welding has the good application potential of reconstitution in nuclear power field.

Plamen Georgiev, Marina Nicolova, Irena Spasova et al.

Copper slag, a by-product of copper ore and concentrate smelting, is rich in non-ferrous metals; therefore, it has been considered a valuable raw material in recent years. This study aimed to compare the extraction of zinc, copper, and cobalt from two types of copper slag from a dump located near the village of Eliseyna, Bulgaria, which differ in mineralogical composition and chemical content, using indirect bioleaching with a spent medium of <i>Aspergillus niger</i> and <i>Penicillium ochrochloron</i>. Chemical leaching with sulphuric acid revealed that zinc and cobalt existed mainly as an acidic-soluble phase in both types of copper slag. In contrast, it contained 50–75% of the total copper content. Each fungal species was cultivated for one week, and the biomass and the spent medium were separated a week later. Owing to the production of a higher concentration of citric acid, <i>A. niger</i> facilitated more efficient base metal recovery. However, their effective recovery from the acidic-soluble phase required leaching at a 5% pulp density and supplementing the spent medium with sulphuric acid. The temperature played a secondary role. Conclusions: Non-ferrous metal extraction from copper slag exposed to weathering using a spent medium supplemented with sulphuric acid was achieved under milder leaching conditions and with better selectivity. In contrast, slag unaffected by weathering behaved as a refractory due to the worsened results of base metal extraction under similar experimental conditions.

Sulayman Khan, Yunus Azakli, William Pulfrey et al.

A microalloyed (MA) steel, combined with titanium diboride (TiB₂), was utilised to create a unique steel matrix composite (SMC), enhancing the modulus of the MA steel while also improving its strength. Through thermomechanical processing stages, including hot rolling and plane-strain compression (PSC) testing, followed by various final cooling methods, a cooling rate of 0.1 °C/s was identified as the most effective for achieving a ferrite–pearlite microstructure, which is suitable for toughness and ductility. With TiB₂ reinforcement successfully incorporated via Fe-Ti and Fe-B additions during vacuum induction melting (VIM), it was observed that the TiB₂ particles were homogeneously dispersed in both 5% and 7.5% nominal volume fraction additions, exhibiting faceted and hexagonal morphology. TiB₂ was found to exert a grain-pinning effect on recrystallised austenite at 1050 °C, as evidenced by the retention of grain orientation from hot rolling, in contrast to the MA steel deformed without the composite reinforcement. Increasing the volume fraction of TiB₂ improved the stiffness and strength of both composite alloys, verified through mechanical testing.

Md. Ashab Siddique Zaki, Tanisha Ahmed, Fahmida Gulshan

Magnesium alloys offer enormous potential as biodegradable implant materials due to their biocompatibility, low density, and bone-like mechanical qualities, making them ideal for orthopedic and cardiovascular implants. However, rapid depreciation in physiological circumstances results in initial mechanical breakdown and hydrogen gas release, which can cause tissue damage. Alloying, thermomechanical processing, and surface treatments are all necessary methods for controlling the degradation rate while retaining implant stability. This study evaluates the effects of double continuous extrusion (DCE) and Gd addition on the microstructural evolution and mechanical and corrosion behavior of the Mg-2Zn alloy. The Mg3Zn3Gd2 secondary phase is produced when Gd and DCE are combined, as shown by XRD and microscopy. Refined microstructures less than 2 µm are produced as a result of improving corrosion behavior and mechanical characteristics. The DCE sample of Mg-2Zn-1Gd has yield stress (YS), ultimate tensile strength (UTS), and elongation values of 350 MPa, 425 MPa, and 20 %, respectively, due to refined grain and secondary phase dispersion, which are above the minimal requirement for biodegradable implants. Precipitation strengthening and Hall-Petch strengthening are responsible for the hardness enhancement with DCE. However, because of the coupling between the Mg matrix and the Mg3Zn3Gd2 particles, galvanic corrosion accelerates. Interestingly, DCE reduces galvanic effects and improves surface protection, resulting in a lower corrosion rate compared to as-cast Mg-2Zn and Mg-2Zn-1Gd and extruded samples. All of them suggest that double continuously extruded Mg-2Zn-1Gd alloy has a bright future in biodegradable applications, particularly in orthopedic implants.

Maxim Valerianovich Egorov

Integrated business structures are currently the main drivers of economic development, saturating the market with technological solutions and innovations. The quality of management decisions and the efficiency of business operations are significantly improved by the implementation of modern intelligent economic information systems in the corporate environment. A fundamental transformation has occurred: whereas a century ago the global economy was driven by giants in oil refining, mining, metallurgy, and mechanical engineering, today digital technology companies occupy leading positions in global rankings.

Grigory S. Rochev

The article discusses the concept of digital transformation of industry, its impact on the economy and the need for an integrated approach to enterprise management based on modern information technologies and data analysis methods. The author clarified the definition of “digital transformation of an enterprise” from the perspective of transforming the internal environment of enterprise management, described the characteristics of the main elements of digital transformation of an enterprise. The study provides an overview of the implementation of digital technologies in the domestic industry, shows interim results. Particular attention is paid to the use of analytical tools and digital twins in various industries, including metallurgy, chemical, energy, mechanical engineering, mining, food and pharmaceutical. The possibility of digital transformation of an enterprise is scientifically substantiated. Enterprise management in the context of the new fourth industrial revolution is impossible without the use of digital technologies. At the same time, the introduction of digital technologies leads to the need to form new business models and processes; communication mechanisms; changes in the organizational structure, organizational culture of enterprises – into a digital culture.

Jabbarbergenov Madiyar Jollibayevich, Madenov Berdimurat Dauletmuratovich, A. Mamataliev et al.

In this research, the chemical composition and physicochemical properties of vermiculite from the Tebinbulak deposit, located in the Karauzyak district of the Republic of Karakalpakstan, were comprehensively investigated. The analyses conducted using X-ray diffraction (XRD), scanning electron microscopy (SEM), and energy-dispersive spectroscopy (EDS) revealed that the studied vermiculite belongs to the class of layered magnesium–aluminum and magnesium–iron aluminosilicates. The crystal structure of the mineral contains interlayer water molecules, which contribute to its distinctive physicochemical behavior. Owing to its broad range of applications—including in construction, the food and chemical industries, oil and gas extraction, nuclear energy, environmental protection, metallurgy, mechanical engineering, railway car manufacturing, shipbuilding, mining, and agriculture—the industrial relevance and economic value of vermiculite are steadily increasing.

Aisheng Jiang, Zhanqiang Liu, Jinfu Zhao

The design of the chip flute in indexable insert drills significantly influences chip removal efficiency, drill diameter deflection, and drill deformation in the metal drilling process, which are crucial for maintaining drill stability and minimizing deviations in the diameter of the drilled hole. However, traditional chip flute designs fail to meet production standards when drilling deep holes in 42CrMo, particularly at depths reaching up to seven times the hole diameter. This study introduces an innovative optimization method for the chip flute design of indexable insert drills specifically intended for metal deep-cutting applications, which involves refining both the cross-sectional and circumferential profiles of the chip flute. A novel combined cross-section for the chip flute was developed and tested against the conventional double U-profile in drilling experiments on 42CrMo. Based on the chip shape of the inner and outer inserts, the inner insert flute section is designed into a U-shaped section and the outer insert flute section is designed into trapezoidal section, respectively, so as to increase the proportion of the effective chip removal area in the chip flute, which reduces the chip flute section area and increases the core thickness of the tool holder. Additionally, the circumferential profile was enhanced through orthogonal simulation experiments. The findings revealed that the drill diameter deflection using the newly designed combined cross-section was reduced by 21.76% compared to the traditional double U-profile in the metal drilling process. The indexable insert drill featuring this optimized chip flute configuration exhibited significant improvements in the drill diameter deflection, drill deformation, and drilled hole diameter accuracy, outperforming the standard drill design.

Gina Genoveva ISTRATE, Andreea Liliana LAZĂR, Eliza DĂNĂILĂ

Soil quality is now thought of in a way that emphasises functionality in a broad context, involving soil not only as an environment for crop production but also as an important reservoir for water storage, as a buffer for filtering, transforming, and neutralising pollutants, and as a habitat for plants and animals. Some attributes (pH, soil conductivity, etc.) can be associated with several easily identified threats to soil quality, some of which are interrelated in the same way. For the present work, laboratory analyses were carried out to determine the physico-chemical parameters of soil from different areas. For soil quality analysis, the following parameters were determined: pH, conductivity, acidity, alkalinity, total nitrogen, aluminium,  phosphates, iron, heavy metals, calcium, and magnesium content in the soil.

Yuanbin WANG, Yaru GUO, Jia LIU et al.

The complex underground environment, filled with a large amount of dust and water vapor, and uneven illumination of artificial light source, leads to problems such as low illumination and loss of detail features in images collected by underground monitoring equipment, which seriously affects the real-time performance of mining safety monitoring equipment, is not good for subsequent computer vision tasks, and it is difficult to collect underground data. It is difficult to make paired low-light image data sets for model training. To solve these problems, a low illumination image enhancement algorithm based on CycleGAN is proposed. In view of the difficulty of collecting paired data set under mine, CycleGAN network is used for unsupervised learning. In order to improve the detail feature extraction ability of the generator network, the image enhancement network was constructed by using the Parameter-Free Attention Mechanism (simAM) and the Dual-Channel Attention Mechanism (CBAM) to improve the anti-interference ability of the model in complex background, so that the model could recover the image detail features better, which improved the anti-interference ability of the model under complex background and made the model recover the detail features better. A luminance enhancement module based on residual cavity convolution is introduced to increase the luminance of the image while enlarging the receptive field of the generator network, which is conducive to detail recovery and visual quality improvement. Patch-GAN is apply for the discriminator of the network, and the input is mapped into a matrix to pay more comprehensive attention to the details of different regions of the image, and improve the discriminator's resolution of image details. Experimental results show that compared with the CycleGAN algorithm, the proposed method improves the peak signal-to-noise ratio (PSNR), structural similarity (SSIM), information entropy and visual information fidelity (VIF) by 11.31%, 8.07%, 2.58% and 6.18% on average.