Search Results (86)

Waste aluminum dust generated from aluminum refining and machining processes contains high fractions of Al₂O₃ and SiO₂, making it a potential secondary aluminosilicate resource for zeolite synthesis. In this study, waste aluminum dust was converted into functional zeolite materials through dry fusion purification, NaOH-assisted hydrothermal synthesis, acid purification, Si/Al ratio control, and cation-exchange modification. The raw dust was subjected to dry fusion at 1600 °C under an Ar atmosphere to remove metallic impurities and obtain an aluminosilicate precursor. Na-type zeolite was then synthesized using 50 wt.% NaOH solution at 90 °C for 24 h. The as-synthesized Na-type zeolite exhibited an estimated chemical purity of 97.501 wt.% based on measured residual impurities, with Mg, Ca, K, and Ti remaining as major impurities. HCl leaching at 0.25 M for 24 h increased the estimated chemical purity based on measured residual impurities to 98.469 wt.% while retaining the major zeolitic diffraction features. The Si/Al ratio was further controlled using water glass, and the maximum Si/Al ratio of 1.77 was obtained at a Na-type zeolite-to-water-glass mass ratio of 1:2 after reaction at 90 °C for 6 h. The purified and composition-controlled zeolite was subsequently modified with Mg²⁺ and K⁺ ions to prepare Mg-modified and K-modified zeolites. Under fixed batch conditions using a relatively high zeolite dosage and a single initial concentration, Mg-modified zeolite reduced Pb, Hg, Cr(VI), and Cd concentrations from 100 ppm to 0.004, 0.00059, 0.018, and 0.004 ppm, respectively, while K-modified zeolite reduced the total hardness of synthetic hard water from 308.3 to 40.13 ppm as CaCO₃. These results should be interpreted as preliminary batch-performance results under the tested conditions rather than as maximum adsorption capacities or a complete adsorption-mechanism evaluation. Overall, this study demonstrates the feasibility of valorizing waste aluminum dust into purified and cation-modified zeolite materials for potential water-treatment applications. Further adsorption isotherm, kinetic, dosage-dependent, BET surface area, pore-volume, pore-size distribution, and quantitative phase analyses are required to evaluate adsorption capacity, adsorption mechanism, true zeolite phase purity, and framework–performance relationships. Full article

We demonstrate a loop-based deep optical convolutional neural network that reuses a single free-space optical hardware to realize network depth through repeated passes. Convolution is implemented with programmable SLM with Fourier plane kernels, nonlinearity is provided by the photorefractive phase-only response of a BSO crystal and converted to an effective intensity activation via spatial filtering, and pooling is performed optically using demagnified imaging with an iris. On MNIST, the BSO-based nonlinearity improves test accuracy from 90.8% (linear) to 95.7%, with optimal operation. We model realistic optical noises (laser fluctuation, aberration, detector misalignment, and dust) and compare them using an SSIM-normalized severity metric. Under noise at (s = 0.35) on Fashion-MNIST, accuracy drops from 88.53% (clean) to 79.5% (noisy inference); a feature-level noise-aware training strategy recovers performance to 86.87%. Together, these advances demonstrate that a compact, loop-based hybrid DOCNN, completed with simple optical nonlinearities, simplified pooling, and noise-aware learning, can improve accuracy under realistic conditions. Full article

The assessment of tunnel blasting effects traditionally relies on manual inspection and contact measurements, which are subjective, inefficient, and lack comprehensive quantification. To address this, this study proposes a novel closed-loop framework that integrates multi-view 3D reconstruction with intelligent recognition for quantitative blasting evaluation and parameter optimization. Rather than claiming novelty in these basic computer vision algorithms, the novelty of this work lies in their tunnel blasting oriented integration: reconstructed geometry is converted into blasting relevant indicators and then linked to parameter adjustment decisions within a closed-loop workflow. The framework begins with a standardized image acquisition workflow designed for challenging tunnel environments (e.g., dust, uneven light), followed by image enhancement using histogram equalization and bilateral filtering. A key improvement is an enhanced SIFT feature matching strategy, which incorporates a BBF optimized K-D tree and RANSAC to achieve robust correspondence establishment on texture-repetitive rock surfaces. This enables the generation of high-precision 3D models of the tunnel face via Structure from Motion (SfM) and Poisson surface reconstruction. From these models, quantitative indices are automatically extracted: rock mass structural planes are clustered via the ISODATA algorithm, structural traces are delineated using a minimum cost path method, and face flatness is evaluated through curvature analysis. These indices form the basis for intelligent blasting assessment. Crucially, the assessment results are directly fed back to optimize blasting parameters (e.g., adding cut holes, adjusting auxiliary hole spacing). Field application in the Huangtai Tunnel demonstrated that this closed-loop framework significantly improved face flatness (achieving over 50% improvement in the high-curvature area ratio) and contour control. Further verification in the Donghongshan Tunnel showed that the proportion of the sharp feature region decreased from 20.3% to 7.9% after optimization. The proposed framework transitions blasting management from empirical judgment to a data driven, intelligent optimization process, offering a scalable solution for enhancing quality and efficiency in tunnel construction. Full article

This paper presents a theoretical engineering feasibility analysis of the RK-X photocatalytic process for In Situ Resource Utilisation (ISRU) on Mars. Experimental validation under simulated Martian conditions is the essential next step before any mission deployment claim can be made. The RK-X process converts the two most abundant Martian resources, atmospheric carbon dioxide (CO₂) and subsurface water ice (H₂O), into formic acid (HCOOH) and oxygen (O₂) through a fulvic acid-based photocatalytic cycle validated at the industrial scale in Hungary. A reference module processing 10 tonnes of CO₂ per Earth year yields 10.459 tonnes of formic acid and 3.636 tonnes of oxygen, sufficient to sustain a six-person crew for approximately two Earth years with a 198% safety margin over nominal respiratory demand. The economic analysis indicates that importing equivalent oxygen from Earth costs $1.82–$3.64 million per year; equivalent energy storage (Li-ion) costs $30.5–$61 million for one-time use. Formic acid stores 15.25 MWh of energy in ambient-stable liquid form at a round-trip efficiency of 68.64% without cryogenic infrastructure. A photovoltaic array of 55.37 m² provides the primary energy source; a kilowatt-class nuclear fission reactor constitutes the strategic opportunity for continuous, dust-storm-immune operation with free thermal co-generation. Three critical research gaps have been identified requiring laboratory validation before Mars deployment: (i) catalyst performance at the Martian CO₂ partial pressure (p(CO₂) < 10 mbar, T = 15 °C); (ii) water ice and dry ice extraction at an operational scale; and (iii) integrated closed-loop system demonstration. Built on Earth-proven chemistry with identified, addressable development pathways, the RK-X process theoretically resolves the problems of oxygen supply, seasonal energy storage, water management, and cryogenic infrastructure within a single closed-loop chemical cycle. Full article

A compact millimeter-wave radar system operating at 33 GHz is presented for integration on small unmanned aerial systems (UAS) and for ground-based counter-UAS reconnaissance. The design is specifically motivated by civil-sector agricultural applications, where large-payload crop-dusting and precision-spraying drones operating under FAA 14 CFR Part 137 require lightweight sense-and-avoid radar that conforms aerodynamically to existing aircraft or ground vehicles. The system is based on a 36-element hemispherical conformal phased array of crossed half-wave dipole radiators that generate right-hand circular polarization (RHCP) on transmit and selectively receives left-hand circular polarization (LHCP) echoes from targets, providing passive first-stage suppression of co-polarized rain and ground clutter. A Linearly Constrained Minimum Variance (LCMV) digital beamformer, applied to per-element analog-to-digital converter (ADC) outputs, delivers closed-form beam weights that enforce a distortionless response at each scan direction while globally minimizing sidelobe power. The formulation resolves the main-beam drift caused by the ill-conditioned re-scaling step in iterative Chebyshev tapering, achieving sidelobe levels below $-20$ dB with main-beam peaks within $0.1$° of their commanded angles across all evaluated positions. Mutual coupling between array elements is modeled analytically using the induced-EMF method, yielding a $36\times 36$ impedance matrix whose off-diagonal entries are at most 8.2% of the element self-impedance at the minimum inter-element separation of 2.70 $\lambda$. A closed-form decoupling matrix is applied to the receive manifold prior to LCMV weight computation. Seven simultaneous independent receive beams covering $0$°–$60$° elevation are formed from a single data snapshot. A Scaled Conjugate Gradient neural network classifier, trained on radar-equation-scaled micro-Doppler features following Swerling I–IV radar cross-section (RCS) fluctuation statistics, achieves overall classification accuracy above 85% across five target classes. The five classes comprise two bird-signature classes (SW-I and SW-II), two UAV-signature classes (SW-III and SW-IV), and a clutter class. The design is entirely simulation-based; experimental validation using a sub-array prototype is identified as the primary direction for future work. Full article

Polarimetric descattering imaging has attracted growing interest due to its fundamental physical significance and potential applications. While deep learning has accelerated its development through powerful feature extraction and inference capabilities, existing methods still face limitations in practical scenarios, particularly under dynamic non-uniform scattering conditions such as cement dust environments. To address this, we propose a deep neural network based on the Mueller matrix model that effectively integrates polarization evolution information with deep learning. Specifically, local concentrations of the scattering medium in non-uniform cement dust are characterized by the evolution of the degree of linear polarization (DoLP), which is converted into pixel-wise weight biases to generate customized Mueller matrices adaptable to varying concentrations. The network predicts a pixel-wise dust concentration map and applies the corresponding concentration-specific Mueller matrix to each pixel for polarization-aware dehazing, ensuring physical consistency with Mueller matrix calculus throughout inference. This framework is further enhanced by a physics-constrained optimization loss and multi-scale feature fusion. Experimental results demonstrate the method’s effectiveness and superiority in diverse dynamic non-uniform cement dust environments. Full article

Lead cake forms from dust as a result of the gas cleaning process during copper smelting. The objective of this study was to develop equipment and technology for a continuous hydrometallurgical method for extracting osmium from the lead cake. In this method, leaching is carried out using an aqueous solution of hydrogen peroxide and sulfuric acid. During the leaching, rhenium is converted into an acidic solution from which rhenium can be easily extracted into a marketable product. Osmium is predominantly converted into a solution, the processing of which, including the extraction of osmium into a marketable product, will be published later. A unit for leaching osmium–rhenium-containing cake with continuous loading for leaching, continuous feeding of leaching solutions, and continuous discharge of the leaching slurry was created. Using the simplex experimental design method, the dependence of osmium recovery on the consumption rates of hydrogen peroxide and sulfuric acid and the leaching duration was studied. Near-optimal leaching conditions were as follows: 68–70 mL of 30% hydrogen peroxide and 7 mL of concentrated sulfuric acid per 100 g of cake, 55 min of leaching, and a specific column throughput of 100 g of cake per 55 min. Nine experiments achieved 96.5% osmium recovery. Full article

This study presents the engineering design and system-level modeling of a high-frequency power supply architecture for electrostatic precipitators intended to improve particulate removal efficiency and operational stability. Atmospheric air pollution by fine particulate matter (PM2.5) remains one of the most critical challenges in environmental protection and public health. Although electrostatic precipitators (ESPs) are widely used for industrial gas cleaning, the efficiency and stability of conventional 50 Hz power supplies are limited under conditions of strongly nonlinear corona discharge and high-resistivity dust. This paper presents the development and investigation of an advanced high-frequency power supply system for electrostatic precipitators based on a coupled electrical–electrophysical mathematical model. The work follows an engineering design methodology that integrates converter topology selection, electrophysical modeling of corona discharge, and control-oriented system optimization. The proposed model provides a unified description of electric field formation, space charge accumulation, ion transport, and particle motion in the corona discharge region. The simulation results show that in the operating voltage range of 10–100 kV, the electric field strength reaches (2–5)·10⁶ V/m, the ion concentration stabilizes in the range of 10¹³–10¹⁵ m⁻³, and the particle drift velocity increases from approximately 0.05 to 0.3 m/s, leading to an increase in collection efficiency from about 55% to 93%. It is demonstrated that the proposed system ensures stable output voltage regulation within ±2.5–5% even under strongly nonlinear load conditions. The use of an LC output filter (C = 1–10 nF, L = 10–100 mH) reduces the voltage ripple from about 14% to 1.4–4.8% and significantly improves the transient response. In addition, adaptive adjustment of the pulse repetition frequency in the range of 10–200 kHz makes it possible to reduce energy consumption by 12–18% while simultaneously increasing the collection efficiency by 8–15%. The obtained results confirm that the proposed high-frequency power supply architecture provides a physically well-founded and energy-efficient solution for improving the environmental performance and operational stability of electrostatic precipitators. Full article

To address the limited environmental perception capability and the difficulty of achieving consistent and efficient representation of the workspace feasible domain caused by high dust concentration, uneven illumination, and enclosed spaces in underground coal mines, this paper proposes a digital spatial construction and representation method for underground environments by integrating RGB-D depth vision with ORB-SLAM3. First, a ChArUco calibration board with embedded ArUco markers is adopted to perform high-precision calibration of the RGB-D camera, improving the reliability of geometric parameters under weak-texture and non-uniform lighting conditions. On this basis, a “dense–sparse cooperative” OAK-DenseMapper Pro module is further developed; the module improves point-cloud generation using a mathematical projection model, and combines enhanced stereo matching with multi-stage depth filtering to achieve high-quality dense point-cloud reconstruction from RGB-D observations. The dense point cloud is then converted into a probabilistic octree occupancy map, where voxel-wise incremental updates are performed for observed space while unknown regions are retained, enabling a memory-efficient and scalable 3D feasible-space representation. Experiments are conducted in multiple representative coal-mine tunnel scenarios; compared with the original ORB-SLAM3, the number of points in dense mapping increases by approximately 38% on average; in trajectory evaluation on the TUM dataset, the root mean square error, mean error, and median error of the absolute pose error are reduced by 7.7%, 7.1%, and 10%, respectively; after converting the dense point cloud to an octree, the map memory footprint is only about 0.5% of the original point cloud, with a single conversion time of approximately 0.75 s. The experimental results demonstrate that, while ensuring accuracy, the proposed method achieves real-time, efficient, and consistent representation of the 3D feasible domain in complex underground environments, providing a reliable digital spatial foundation for path planning, safe obstacle avoidance, and autonomous operation. Full article

Squirrel-cage induction generators often perform better without a mechanical speed sensor. Eliminating an encoder or resolver removes one of the most fragile and failure-prone components, while modern control algorithms can estimate speed with sufficient accuracy. Shaft-mounted sensors are vulnerable to heat, vibration, dust, moisture, and electrical noise; they require precise mounting and additional cabling and typically fail long before the machine itself. In many industrial and marine applications, unplanned shutdowns are more often caused by damaged sensors or cables than by the generator. Unlike sensorless speed-detection methods developed for motoring operation, the proposed approach targets the generator mode, where both phase currents and the DC-link voltage are measured. It uses two indicators: the magnitude and sign of the active current, and the instantaneous rise in DC-link voltage when the converter output frequency matches the machine’s shaft speed. Because active current remains negative over a wide frequency range during start-up, its sign change alone cannot uniquely identify the synchronization point. In generator operation, however, the DC-link capacitor voltage provides an additional criterion: the speed at which power reverses sign, indicated by a change in the sign of the DC-voltage derivative. As the inverter frequency approaches the machine rotational frequency from below, the DC voltage increases, reaches a maximum at maximum slip, and then decreases once the inverter frequency exceeds the machine speed. The article demonstrates how these signals can be used in practice to identify the rotational speed of a squirrel-cage generator. Full article

The principal global sources of platinum-group elements (Os, Ir, Ru, Rh, Pt, Pd), collectively referred to as PGEs, are magmatic Ni-Cu sulfide deposits associated with large, layered intrusions, such as the Bushveld Complex. Recent exploration efforts have identified rock types with elevated PGE concentrations, although their potential remains uncertain. This comprehensive review synthesizes the current knowledge regarding potential sources from both natural magmatic and anthropogenic activities, as well as the environmental risks associated with the Pt, Pd, and Rh sub-group, or PPGEs. The order of Pd > Pt > Rh content in emitted particulates has been documented in dust and soil along roadsides, whereas in Fe-Ni laterite, Pt tends to accumulate residually at the top of profiles due to the higher mobility of Pd compared to Pt and Rh. The greater mobility and transfer of Pd are evidenced by higher bioaccumulation factors for Pd in plants and crops, with a higher Pd content observed in roots than in shoots. The effects of chronic occupational exposure to Pt compounds, such as allergic reactions affecting the skin and respiratory system of workers, are well-documented. Although no established permissible limits for Pt, Pd, and Rh in soil, water, or plants exist within major regulatory frameworks, the increasing applications of PPGEs and the use of Pd in catalytic converters (due to its lower cost) underscore the need for further studies on the recycling of spent catalytic converters, health impacts, ecotoxicological assessments, and the application of current technological advances to mitigate exhaust emissions. Full article

Maintaining the structural integrity of conveyor belts is essential for safe and reliable mining operations. However, these belts are susceptible to longitudinal tearing and surface degradation from material impact, fatigue, and deformation. Many computer vision-based inspection methods are inefficient and unreliable in harsh mining environments characterized by dust and variable lighting. This study introduces a smartphone-driven defect detection system for the cost-effective, geometric inspection of conveyor belt surfaces. Using Apple’s iPhone 12 Pro Max (Apple Inc., Cupertino, CA, USA), the system captures 3D point cloud data from a moving belt with induced damage via the integrated TrueDepth camera. A key innovation is a 3D-to-2D projection pipeline that converts point cloud data into structured representations compatible with standard 2D Convolutional Neural Networks (CNNs). We then propose a hybrid deep learning and machine learning model, where features extracted by pre-trained CNNs (VGG16, ResNet50, InceptionV3, Xception) are classified by ensemble methods (Random Forest, XGBoost, LightGBM). The proposed system achieves high detection accuracy exceeding 0.97 F1 score in the case of all proposed model implementations with TrueDepth F1 score over 0.05 higher than RGB approach. Applied cost-effective smartphone-based sensing platform proved to support near-real-time maintenance decisions. Laboratory results demonstrate the method’s reliability, with measurement errors for defect dimensions within 3 mm. This approach shows significant potential to improve conveyor belt management, reduce maintenance costs, and enhance operational safety. Full article

This study assesses air quality and health impact in Hanoi, Vietnam, using the Community Multiscale Air Quality (CMAQ) model and health impact assessment to evaluate the effectiveness of traffic emission reduction strategies under two scenarios. An updated emission inventory was used as the input data for the CMAQ model. The Weather Research and Forecasting (WRF-CMAQ) model (version 5.4), incorporating the CB6 chemical mechanism, was applied alongside a calibrated meteorological model to simulate pollutant dispersion. The model achieved strong performance in PM_2.5 simulation, with a correlation coefficient (R) of 0.78, an index of agreement (IOA) of −0.5, a Normalized Mean Bias (NMB) of 7.11%, and a normalized mean error (NME) of 28.51%. Seasonal analysis revealed higher concentrations of CO, NO₂, O₃, and SO₂ in January compared to July, driven by traffic and industrial emissions. Improved air quality in July was attributed to favorable meteorological conditions, such as increased rainfall and clean airflows from the sea. Spatial distribution highlighted elevated pollutant levels in urban areas, while PM_2.5 was significantly influenced by long-range transport and atmospheric processes. However, fine dust concentrations remained high in suburban areas, driven by secondary emissions and nearby industrial zones. An emission reduction scenario based on the Hanoi city policy decree focusing on traffic sources demonstrated its potential to reduce NO₂, SO₂, and PM_2.5 concentrations, though the impacts varied across time and space. Health impact due to population exposure to PM_2.5 shows that the densely populated suburbs surrounding the urban core have the largest impact in terms of mortality and cardiovascular diseases hospitalization. As PM_2.5 has the largest impact on these two health endpoints, only PM_2.5 impact assessment is performed. Health impact due to air pollution is higher in January (dry season) with estimated 625 deaths and 124 cardiovascular diseases (cvd) hospitalization as compared with estimated 94 deaths and 18 cvd hospitalization in July (wet season). One of the research questions posed by the city authority is whether converting diesel buses to electric buses can yield environmental and health benefits. Our work shows that the scenario based on Hanoi city decree of replacing 50% of fossil fuel combustion buses with electric buses by 2035 does not yield perceptible change in mortality health effect. This is due to emission from buses being small as compared to those from the whole transport sector and other sectors. This study emphasizes the need for integrated, targeted emission control strategies to address spatial and temporal variability in pollution. The findings offer valuable insights for policymakers to develop effective measures in urban planning for improving air quality and protecting the health of people in Hanoi. Full article

This work investigates how fractionality affects the dynamical behavior of dust-acoustic shock waves that arise and propagate in a depleted-electron complex plasma. This model consists of inertial negatively charged dust grains and inertialess nonextensive distributed ions. Initially, the fluid model equations that govern the propagation of nonlinear dust-acoustic shock waves are reduced to the integer Burgers-type equations using the reductive perturbation method. Thereafter, the integer Burgers-type equations are converted to the fractional cases using a suitable transformation. For analyzing this fractional family, both the Tantawy technique and the new iterative method are implemented within the Caputo sense framework. These methods can produce highly accurate analytical approximations without necessitating stringent assumptions or intricate computational processes, in contrast to other similar methods. Numerical examples and the calculation of the absolute error demonstrate the efficacy of the suggested methodologies, emphasizing their superior precision and swift convergence. Full article

The paper explores the application of heat pipes in thermal management for efficient heat dissipation, particularly in electrical equipment with high heat loads. Heat pipes are devices that transfer heat with high efficiency through the phase transition of the working medium (e.g., water, alcohol, ammonia) between the evaporator and the condenser, while they have no moving parts and are distinguished by their simplicity of construction. Different types of heat pipes—gravity, capillary, and closed loop (thermosiphon loop)—are suitable according to specific applications and requirements for the working position, temperature range, and condensate return transport. An example of an effective application is the removal of heat from the internal winding of a static energy converter transformer, where the use of a gravity heat pipe has enabled effective cooling even through epoxy insulation and kept the winding temperature below 80 °C. Other applications include the cooling of mounting plates, power transistors, and airtight cooling of electrical enclosures with the ability to dissipate lost thermal power in the order of 102 to 103 W. A significant advantage of heat pipes is also the ability to dust-tightly seal equipment and prevent the build-up of dirt, thereby increasing the reliability of the electronics. In the field of environmental technology, systems have been designed to reduce the radiant power of fireplace inserts by up to 40%, or to divert their heat output of up to about 3 kW into hot water storage tanks, thus optimising the use of the heat produced and preventing overheating of the living space. The use of nanoparticles in the working substances (e.g., Al₂O₃ in water) makes it possible to intensify the boiling process and thus increase the heat transfer intensity by up to 30% compared to pure water. The results of the presented research confirm the versatility and high efficiency of the use of heat pipes for modern cooling requirements in electronics and environmental engineering. Full article