Zhenmin LUO, Shangyong ZHOU, Shuangming WANG
et al.
Against the backdrop of escalating global energy crises and driven by the national strategic imperative to enhance domestic oil and gas supply, in-situ pyrolysis technology for oil-rich coal has garnered unprecedented attention in China due to its advantages of lower pollution, minimal geological disturbance, and high potential for large-scale deployment. Nevertheless, the inherent challenges of drilling-based in-situ pyrolysis including non-visualized processes, personnel inaccessibility, and complex subsurface reaction conditions, pose significant safety risks to coal production, constituting critical constraints for large-scale technological deployment. A comprehensive understanding of oil/gas production characteristics and their combustion/explosion hazards, the regulation mechanisms of the in-situ pyrolysis reactions, the fracture propagation laws during pyrolysis, the oil/gas migration mechanisms, as well as the environmental impacts and corresponding prevention/control technologies, forms the essential foundation for ensuring stable operation and safety risk mitigation in in-situ pyrolysis of oil-rich coal. Nevertheless, key knowledge gaps persist: The characteristics of oil/gas production and their associated combustion/explosion risks remain unclear; The heat transfer laws and transport models within the pyrolysis zone are inadequate; and the migration patterns of oil/gas products under multi-field coupling effects are not yet fully elucidated. Building upon the refinement of these fundamental theories, the development of fracture-sealing materials capable of simultaneously preventing gas escape and stabilizing overlying strata, the exploration of sealing techniques for discontinuous fracture spaces, the invention of shielding/isolation technologies for pyrolysis reaction chambers, the advancement of an integrated air-space-ground multi-dimensional monitoring system with efficient data collection and processing, and the establishment of an intelligent and automated control system for the in-situ pyrolysis reaction are crucial for providing safety assurances for the technology's promotion. Although relevant theoretical, technological, and equipment research has been conducted globally, it has not yet fully addressed the pre-control safety requirements of the in-situ pyrolysis process for oil-rich coal.
The complex pore structure and organic matter composition of coal significantly affect the storage and transportation characteristics of gas, and the role of soluble organic matter is still lacking in in-depth research. This study, represented by tetrahydrofuran-2-ol (C4H8O2), explores the effect of small molecule organic compounds on coal adsorption of CH4 and CO2 through quantum chemical simulations. The static potential of a single molecule was determined through quantum chemistry calculations. Detailed analysis was conducted on the adsorption heat, mean square displacement, radial distribution function, and adsorption energy distribution during the adsorption process. The results indicate that the excessive adsorption capacity of coal for CO2 is always higher than that for CH4. Organic small molecules significantly reduce the gas adsorption capacity and adsorption heat of coal, weaken the interaction between heteroatoms and adsorbate molecules, and have a significant impact on CO2 adsorption, thereby significantly reducing the interaction between CO2 and coal molecules and weakening the displacement effect of CO2 on methane. At 6 MPa, its impact on CO2 adsorption is minimal. The results of this study contribute to a better understanding of the occurrence mechanism of coalbed methane, providing theoretical support for optimizing pre extraction gas technology and assisting in coal mine safety and efficient production.
To confirm the impact of Li2O and Na2O on the structure and property of CaO-SiO2-B2O3 based fluorine-free mold fluxes, devices including rotary viscometer, X-ray diffraction, combined with Fourier transform infrared (FTIR) spectroscopy were applied in the present study. From FTIR results, it was noted that with the addition of Li2O (0-3 wt%) and Na2O (4-12 wt%), there would be simpler Si-O and B-O structural units formed. However, all the structure units were intensified when the content of Li2O (4 wt%) was added in slag. By the accumulation of Li2O and Na2O in mold fluxes with various BaO content, the viscosity at 1300℃ decreased generally, showing that viscosity was influenced by the combination of structure and superheat, and superheat gradually played a dominant role as Li2O reached 4 wt%. Depending on the viscosity-temperature curve, all samples showed acidic slag characteristics and the decrement activation energy of slag came as the increment of Li2O and Na2O at the basicity 1.15 in overall, which were beneficial for the play of slag lubrication function. The effect of Li2O on crystallization of fluorine-free mold fluxes with 5 wt% of BaO were analyzed that all the diffraction peaks displayed in the XRD patterns corresponded to the standard peaks of Ca2SiO4 and Ca11Si4B2O22. Li2O has an imperative role in all samples that enhanced the crystallization performance of the mold fluxes in the low-temperature zone.
Mining engineering. Metallurgy, Materials of engineering and construction. Mechanics of materials
In the iron ore agglomeration industry, extensive research has been conducted on various drying techniques. Drying iron ore briquettes is essential to provide the initial strength needed for freshly prepared briquettes. Briquettes with the composition of iron ore fines (16.5%), (Linz-Donawitz) sludge (47.2%), flue dust (28.3%), bentonite (2%), and cement (6%) were dried using hot air oven drying, microwave drying and infra-red drying under controlled heating conditions. After drying the briquette samples were stored under ambient atmospheric conditions for 7 days. The open air drying of the briquettes is the cheapest method available for the curing of briquettes, but it mainly depends on sunlight. It adversely affects the continuous supply of iron ore briquettes for the steel industry in the winter and rainy seasons. This work aims to determine the most efficient drying technique with low carbon emission as compared to high temperature drying using conventional drying techniques. The briquettes should have some initial strength so they do not break while being transported from one place to another for steel production. These briquettes should be strong enough so that they do not disintegrate before the start of the reduction process in the blast furnace. Infra-red drying at 120°C produced the highest strength of 4.195 N/mm 2 (MPa), surpassing the strength (3.680 N/mm 2 ) achieved by hot air drying. This method of low temperature drying not only facilitates the production of high-quality dried products but also contributes to the creation of eco-friendly briquettes with reduced carbon emission. Here this study employs low temperature drying as a sustainable drying practice.
Although it has been more than four decades that the first components-based software development (CBSD) studies were conducted, there is still no standard method or tool for component selection which is widely accepted by the industry. The gulf between industry and academia contributes to the lack of an accepted tool. We conducted a mixed methods survey of nearly 100 people engaged in component-based software engineering practice or research to better understand the problems facing industry, how these needs could be addressed, and current best practices employed in component selection. We also sought to identify and prioritize quality criteria for component selection from an industry perspective. In response to the call for CBSD component selection tools to incorporate recent technical advances, we also explored the perceptions of professionals about AI-driven tools, present and envisioned.
Autonomous and smart mines are predicted to become more prevalent. Automation has undeniable benefits in the mining industry, especially in terms of safety. However, automation has also led to unforeseen implications for individuals, organisations and communities. This study undertakes a systematic review of research on the impacts of automation in the mining context. A total of 94 documents that dealt with issues related to humans, safety and communities were found. Documents were analysed using both manual and natural language processing techniques. The review revealed the main concerns the industry must face for the successful implementation of automation, with interoperability and inadequate wireless networks identified as the most significant challenges. Key themes for individuals were workload, cognitive load, communication, acceptance of automation and trust. Task changes and culture were the most predominant issues at the organisational level. Impacts on employment and indigenous communities were highlighted at the community level. The emergence of advanced technologies and interoperability issues have implications for implementing of smart or intelligent mining. Human factors, precisely situation awareness and workload, have far-reaching consequences for safety and productivity because automation is becoming more complex. Moreover, not quantifying community impacts affects how companies can meet their corporate social responsibility commitments.
For the problem of interference between shearer drum and hydraulic support guard plate, an interference state intelligent recognition method for shearer drum and hydraulic support guard plate of improved YOLOv5s algorithm is proposed. The use of boundary constraint and non-linear contextual regularization based on the group's previous proposed method of defogging and dust removal to clarify the video image, improve the quality of the monitoring video image of the comprehensive mining face. The YOLOv5s model is improved by replacing the ordinary convolutional Conv in the YOLOv5s backbone network with Ghost convolution, the improved algorithm greatly reduces the number of model parameters and improves the model recognition speed. At the same time, the coordinate attention mechanism is introduced to improve the model's ability to extract the features from the guard plate and shearer, and improve the recognition accuracy. The soft non-maximum suppression algorithm (Soft-NMS) anchor frame screening method is used to reduce the problem of missed detection due to overlapping guard plates. For the problem of determining the interference state of shearer drum and hydraulic support guard plate, the method for determining anchor box overlap degree between hydraulic support guard plate and shearer drum. The improved YOLOv5s algorithm is compared with YOLOv5s and YOLOv3-tiny algorithm. The results indicate that compared with the original YOLOv5s model, the recognition accuracy of this method has been improved by about 8.1%, and GFLOPs have been reduced by 1.86 times. mAP@.5 was increased to 97.2%, and the average recognition speed is 169 frames/s. The improved YOLOv5s algorithm is used to validate the interference state recognition effect for video images of shearer drum and hydraulic support guard plate in in actual fully mechanized mining faces, and the results show that the recognition accuracy of interference state between the coal shearer drum and the hydraulic support guard plate is 96%.
Atmospheric-controlled induction-heating fine-particle peening (AIH-FPP) was applied to low-alloy, low-carbon steel AISI 4120 to achieve rapid carburizing within minutes and enhance fatigue strength. The shot particles used during AIH-FPP were specifically created by mechanically milling a mixture of steel particles and carbon powders. The specimens were analyzed using laser microscopy, optical microscopy, X-ray diffraction for residual stress measurement, a micro-Vickers hardness tester, and an electron probe micro-analyzer. Axial fatigue tests and fracture surface analyses via scanning electron microscopy and energy-dispersive X-ray spectroscopy were also conducted. AIH-FPP effectively transferred carbon to the treated surface and facilitated its diffusion in a short period, creating a martensite layer with high hardness and compressive residual stress, which resulted in increased fatigue strength. The fatigue strength of the AIH-FPP-treated specimens was influenced by the conditions of reheating and quenching, which altered the prior austenitic grain size and stress concentration at the specimen surface. Notably, specimens treated with AIH-FPP followed by reheating and water-injection quenching exhibited fatigue strength comparable to that of conventional gas-carburized specimens, owing to the formation of a carburized layer with a small prior austenite grain size.
Federico Williamson, Nadhir Naciff, Carlos Catania
et al.
This work proposes a computationally efficient approach for estimating the magnetization of Fe0.7Ni0.3 body-centered cubic (bcc) nanoparticles (NPs) at room temperature using machine-learning algorithms, in terms of the average magnetic moment per atom, ⟨μ⟩. The magnetization data of isolated NPs were generated using atomistic spin dynamics (ASD) simulations for various nanoparticle shapes (cubes, spheres, octahedra, cones, cylinders, ellipsoids, flakes, and pyramids, with or without nanovoids) and FeNi distributions (random, core-shell, onion, sandwich, and Janus with different boundary planes). More than 1600 NPs were created and split into training and testing sets (70%–30% split), with features including the number of Ni/Fe surface and core atoms, potential energy distributions, pair correlation functions, and coordination distributions. Several machine-learning algorithms, including Random Forest (RF), Elastic Net, Support Vector Regression (SVR), and Gradient Boosting Regression (CatBoost), were applied to predict the average magnetic moment per atom of these NPs. The best-performing models, CatBoost and RF, achieved R2 scores of up to 0.86, demonstrating their accuracy in predicting NP magnetization. Feature analysis highlighted the significance of the interface between Fe and Ni clusters, Fe–Fe interactions, and the presence of Fe on the surface as critical contributors to overall magnetization. Random alloy spherical NPs without porosity exhibited the highest ⟨μ⟩ ∼ 1.6μB due to reduced Ni–Ni interactions. Applying machine-learning methods significantly reduces computational time and memory requirements compared to traditional ASD simulations. This allows for rapid prediction of NPs with desired magnetic properties, making them suitable for various technological applications.
Renan Lima Baima, Tiago Miguel Barao Caetano, Ana Carolina Oliveira Lima
et al.
The primary objective is to emphasize the merits of active methodologies and cross-disciplinary curricula in Requirement Engineering. This direction promises a holistic and applied trajectory for Computer Engineering education, supported by the outcomes of our case study, where artifact-centric learning proved effective, with 73% of students achieving the highest grade. Self-assessments further corroborated academic excellence, emphasizing students' engagement in skill enhancement and knowledge acquisition.
AbstractInnovation plays a critical role in the mining industry as a tool to improve the efficiency of its processes, to reduce costs, but also to meet the increasing social and environmental concerns among communities and authorities. Technological progress has also been crucial to allow the exploitation of new deposits in more complex scenarios: lower ore grades, extreme weather conditions, deeper deposits, harder rock mass, and high-stress environments. This paper discusses the importance of innovation for the mining industry and describes the mechanisms by which it is carried out. It includes a review of the drivers and actors involved and current trends. The digital transformation process that the industry is going through is analyzed, along with other relevant trends that are likely to shape the mining of the future. Additionally, a case study is presented to illustrate the technical and economic implications of developing a disruptive innovation project.
For weathering steel used in building, it is necessary not only to ensure weather resistance, but also to improve the strength and yield ratio. This study investigates the strengthening effect of Ti microalloying on the tested steel by conducting continuous cooling transformation tests of undercooled austenite and comparative tests of microstructure and performance at different coiling temperatures, with 0.07 wt.% Ti added to the weathering building test steel. The results show that, with an increase in cooling rate (0.1~50 °C/s), the room temperature microstructure of different cooling rates gradually transitions as follows: F + P, F + P + B, F + B and B + M; in addition, the hardness increases. Polygonal ferrite and pearlite were obtained by coiling at 650 °C; quasi-polygonal ferrite, acicular ferrite, pearlite and a small amount of bainite were obtained by coiling at 600 °C; and bainite was obtained by coiling at 550 °C. With a decrease in coiling temperature, the strength of the test steel increased, the yield ratio increased, the elongation after fracture decreased and the elongation at the yield point decreased. Compared with those observed at 650 °C, the nano precipitation phases observed in the sample at 600 °C were smaller in size, higher in number and higher in dislocation density. The combined action of second-phase precipitation strengthening and dislocation strengthening increased the strength and yield ratio of the test steel. This study will be helpful in guiding the improvement of strength grades for weathering steel used in building and industrial production.
This work is aimed to investigate the relationship between the texture and tensile properties of the AZ31 Mg alloy by the machine learning method. The texture characteristics parameters, namely the maximum pole intensity (Imax), texture dispersion (D), and texture directivities along the longitudinal direction (PLD) and transverse direction (PTD), are extracted from the (0002) pole figures of the AZ31 Mg alloy. An artificial neural networks (ANN) model to describe the relationship between the texture characteristic parameters and tensile properties is constructed and trained by the data collected from the literature. To validate the reliability and generalization performance of the ANN, 6 samples with different texture characteristics are prepared, and their textures and tensile properties are evaluated through electron backscattered diffraction (EBSD) measurement and uniaxial tensile test, respectively. The results indicate that the ANN model exhibits good prediction performance in yield strength and elongation of the AZ31 Mg alloy when it is applied to the new cases. The correlations between the texture characteristics and tensile properties are analyzed according to the ANN-predicted results. The maximum pole intensity and texture dispersion significantly influence the tensile properties of the AZ31 Mg alloy. With increasing the Imax or decreasing the D, the strength is increased but the elongation is reduced. As increasing the texture directivity along the LD, the tensile properties of the AZ31 Mg alloy show non-monotonic changes. This research presents a correlation model between the texture and mechanical properties of the Mg alloy, which contributes to the development of high-performance Mg alloys.
In order to study the influence of the explosion venting position on the explosion characteristics of methane/air premixed gas, the experimental study on the explosion characteristics of methane/air premixed gas with different explosion venting positions was carried out using the gas explosion experimental platform. The results show that: with the increase of the distance between the explosion vent and the ignition source, the peak speed of explosion flame propagation increases gradually. Except for the condition where the explosion vent is 750 mm away from the ignition source, the peak explosion pressure also varies with the explosion vent and the ignition source. The maximum flame propagation velocity and explosion peak pressure can be generated by the methane/air premixed gas under the experimental conditions that the distance between the explosion vent is 1000 mm from the ignition source, and the peak velocity and pressure are 20.25 m/s and 225.56 kPa. The closer the explosion vent is to the ignition source, the lower the explosion risk of the methane/air mixture in the pipeline. The research results can provide theoretical basis for the design of pressure relief position.