Search Results (385)

Mechanical milling is a relevant preliminary processing operation that is widely used for the reuse of various types of agro-industrial waste. The objective of this study was to conduct milling experiments of hazelnut (Corylus avellana L.) shell waste at different times (0.5, 1 and 1.5 min) and subsequently evaluate the particle size distribution (PSD) of the powders obtained by sieving methodology. In addition, flowability parameters were determined for the particles retained on the sieves, and their morphology and microstructure were examined using several microscopy techniques. The results demonstrated that the hazelnut shells were successfully fractionated under the milling conditions investigated (short milling times ≤ 1.5 min), and the histograms of the PSD exhibited a wide dispersion of sizes (≤1.7 mm). The particles retained from sieve₁₀₀ to residue exhibited poor or no flow, attributable to the high degree of cohesion between them. Morphological analysis based on optical microscopy and image analysis revealed that there was an increase in the aspect ratio parameter when the particle size decreased, meaning that the particles had elongated shapes. Microscopic analysis (SEM, AFM and CLSM) showed that the particles exhibited complex shapes and a comparable microstructure, comprising tightly packed clusters of sclerenchyma cells. From the microscopy images obtained (SEM and AFM), it was inferred that the cracks generated during blade impacts propagate along the middle lamella of the cells, allowing the cluster-like arrangement to be preserved. The CLSM results demonstrated that as the size of hazelnut shell particles decreases, the exposure of lignin on its surface is favored. The findings of this study demonstrate that hazelnut shell waste can be readily pre-processed using a blade grinder, thereby facilitating its reuse in applications that demand fine particle sizes (e.g., bioadsorption of pollutants and the production of biocomposite materials). Likewise, the results concerning the flowability parameters, microstructural arrangement, and morphological features of the different particle fractions obtained are crucial variables that must be considered. These variables significantly influence the possible applications for the revalorization of this type of agro-industrial waste. Full article

This study develops and assesses a hydrophobized fly ash mineral powder as a filler for dense fine-graded asphalt mixtures in Kazakhstan. Fly ash from a local TPP was dry co-milled with a stearate-based modifier to yield a free-flowing, hydrophobic powder that meets the national limits for moisture, porosity, and gradation. SEM shows cenospheres and broken shells partially armored by adherent fines, suggesting an increased micro-roughness and potential sites for binder–filler bonding. Three mixes were produced: a carbonate reference and two fly ash variants, all designed at the same optimum binder content. Compared with the reference, fly ash fillers delivered a markedly higher compressive strength (up to about five times at 20 °C), improved adhesion, and high internal friction, while the mixture density rutting resistance was essentially unchanged. Water resistance indices remained high and stable despite only modest changes in water saturation, and crack resistance improved, especially for the dry ash mixture. The convergence of microstructural, physicochemical, and mechanical results shows that surface-engineered fly ash from a Kazakhstani TPP can technically replace natural carbonate filler while enhancing durability-critical performance and supporting the more resource-efficient use of industrial by-products in pavements. Full article

This study addresses the fabrication challenges associated with producing diverse geometries for concrete dry connections, particularly regarding cost, time, and geometric limitations. The research investigates methods for fabricating precise, rebar-free dry connections in concrete, focusing on stamping and green-state computer numerical control (CNC) milling. These methods are evaluated using metrics such as dimensional accuracy, tool abrasion, and energy consumption. In the stamping process, a design of experiments (DOE) approach varied water content, concrete age, stamping load, and operational factors (vibration and formwork) across cone, truncated cone, truncated pyramid, and pyramid geometries. An optimal age range of 90 to 105 min, within a broader operational window of 90 to 120 min, was identified. Geometry-specific exceptions, such as approximately 68 min for the truncated cone and 130 min for the pyramid, were attributed to interactions between shape and age rather than deviations from general guidance. Within the tested parameters, water fraction primarily influenced lateral geometric error (diameter or width), while age most significantly affected vertical error. For green-state milling, both extrusion- and shotcrete-printed stock were machined at 90 min, 1 day, and 1 week. From 90 min to 1 week, the total milling energy increased on average by about 35%, and at one week end-face (head) passes caused substantially higher tool wear, with mean circumference losses of about 3.2 mm for head engagement and about 1.0 mm for side passes. Tool abrasion and energy demand increased with curing time, and extrusion required marginally more energy at equivalent ages. Milling was conducted in two engagement modes: side (flank) and end-face (head), which were evaluated separately. End-face engagement resulted in substantially greater tool abrasion than side passes, providing a clear explanation for tolerance drift in final joint geometries. Additionally, soil-based forming, which involves imprinting the stamp into soft, oil-treated fine sand to create a reversible mold, produced high-fidelity replicas with clean release for intricate patterns. This approach offers a practical alternative where friction and demolding constraints limit the effectiveness of direct stamping. Full article

Online roll profile measurement technology can measure in real time without changing the rolls, which has advantages that traditional roll profile measurement methods cannot compare with. To improve the accuracy of online roll profile measurement during the rolling process, the influence function method was employed to calculate the deformation of the roller system, and an error compensation model for online roll profile measurement considering the deformation of the roller system was established. Numerical simulations of roller deformation and the error compensation of the roll profile measurement were conducted for different rolling processes. The results show that, during the rolling process, under the combined action of rolling force and bending force, the work rolls undergo deflection deformation and elastic flattening. The pressing process and bending force have a significant impact on the roller system deformation. Roll profile measurement errors are associated with both the deflection deformation and the elastic flattening of the rolls. The axial displacement of the rolls has a negligible effect on the rolls’ deflection and flattening. However, when the rolling mill adopts the axial displacement of the roll process, the roll profile measurement system requires displacement compensation. The magnitude and direction of the compensation should be consistent with the displacement and direction of the corresponding roll. This research is of great significance to improve the accuracy of online roll profile measurement, realize the fine management of mill roll in service, and improve the automation level of rolling mill systems. Full article

A novel and rapid ball-milling approach was developed in this study to efficiently intercalate hexadecyltrimethylammonium bromide (HDTMA-Br) into vermiculite (VMT) within only 15 min. The raw granular VMT (2–3 mm) was first ground into fine powder using an airflow pulverizer. A suspension containing VMT and HDTMA-Br (1 CEC) in deionized water was then subjected to planetary ball milling at 450 r/min (25 °C), followed by washing and drying to obtain organo-vermiculite (OVMT) with a particle size of 44–5 µm. X-ray diffraction, Fourier-transform Infrared Spectroscopy and Thermogravimetric Analysis analyses confirmed successful intercalation, with the basal spacing $d_{\left(001\right)}$ expanding from 1.46 nm to 4.51 nm. Transmission Electron Microscopy observations further revealed partial delamination of lamellar structures and a pronounced reduction in particle size, supporting the structural reorganization induced by the mechanochemical process. In addition, nitrogen adsorption analysis showed that the BET surface area decreased by 4.05 m²·g⁻¹, while the average pore diameter increased by 3.2 nm, indicating the development of a more hydrophobic interlayer environment. Overall, this approach offers a practical route for producing organophilic silicate materials and shows strong potential for wastewater treatment applications, particularly for the adsorption of organic pollutants and heavy-metal ions. Full article

Some sugar factories lack sufficient data collection in their milling production lines, making it challenging to construct data-driven models for predicting initial juice sugar content. This paper proposes an adversarial semi-supervised pre-training and fine-tuning modeling method. The model is first trained on data-rich source production lines (190,000 samples, 500 labels, 1 min sampling for process variables, 3 times/day for sugar content) and then fine-tuned using limited data from the target production line (10,000 samples, 100 labels), effectively utilizing both labeled and unlabeled data to enhance the model’s generalization ability. Ablation experiments were conducted using data from two sugarcane milling production lines. The experimental results validate the effectiveness of each component of the proposed method and its prediction accuracy, achieving a reduction in MSE by 0.067 (23.0%) and MAE by 0.066 (15.7%) compared to the standard pre-trained model. This strategy not only optimizes the prediction of initial juice sugar content for production lines with insufficient data collection but also has the potential to improve the efficiency and quality of sugarcane milling industrial production. Full article

Sunflower meal represents a protein- and fiber-rich by-product of the oil industry with potential application as a natural stabilizer in food emulsions. Building upon previous findings that emphasized the role of protein content in emulsion stability, the present study further investigated the combined effect of protein level and particle size distribution of sunflower meal fractions on the formation and stability of oil-in-water emulsions. Two sets of sunflower meal fractions were prepared from finely milled material, fractionated, and blended in controlled proportions to obtain four protein-enriched (30 ± 1%) and four cellulose-rich (15 ± 1%) fractions, each defined by particle size ranges of 250/200, 200/125, 125/100, and <100 µm. Emulsion stability was evaluated through droplet size analysis, zeta potential measurements, and creaming index determination during seven days of storage. The results demonstrated that both protein content and particle size significantly affected the emulsifying and stabilizing behavior of sunflower meal fractions. For the low-protein group (15%), larger particle sizes (250/200 µm) yielded smaller emulsion droplets (D[4.3] = 66.03 µm) and higher zeta potential values (−15.53 mV), while in the high-protein group (30%), droplet size distribution was more uniform (D[4.3] from 72.13 to 76.29 µm). During seven days of storage, all emulsions exhibited a gradual increase in creaming index, followed by partial stabilization at later time points. Emulsions prepared with sunflower meal fractions of higher-protein content showed consistently lower creaming index values, indicating improved physical stability throughout storage. Overall, the study confirmed that the interplay between composition (protein level) and physical structure (particle size) governs the emulsification efficiency of sunflower meal fractions, providing insights for their potential application as plant-based stabilizers in food systems. Full article

Sorghum flour was milled and fractionated into three particle-size classes, 215–181 µm, 181–96 µm, and <96 µm, then blended with wheat flour at a 30:70 (sorghum:wheat) ratio. The composite flours were evaluated to determine the effects of sorghum particle size on dough-sheet properties and the quality of the resulting noodles. Reducing particle size increased pasting and farinograph parameters, and dough rheological properties improved. The findings indicate that replacing wheat flour with sorghum flour fractions increased gelatinization temperature and gelatinization enthalpy. In addition, the moisture distribution of the dough showed that with the addition of sorghum flour fractions, the closely bound water content of the dough increased. A reduction in particle size led to a significant increase (p < 0.05) in glutenin macropolymer (GMP) content and induced changes in the protein secondary structure of the dough sheets. Noodle quality improved as sorghum particle size decreased, as confirmed by scanning electron microscopy (SEM). Finer particle sizes were associated with lower cooking loss and higher water absorption. Furthermore, the ultra-fine sorghum–wheat composite noodle (WC) attained the highest sensory acceptance after the wheat control (W). Overall, reducing the particle size of sorghum flour markedly improved the functional quality of sorghum–wheat composites. Finer fractions enhanced dough microstructure and gluten network stability, increased thermal stability and pasting robustness, and the resulting noodles exhibited improved cooking performance and sensory scores while retaining favorable texture. Full article

Wet stirred media milling (WSMM) is a popular grinding method used to produce important ultrafine-particle materials, such as pigments, pharmaceuticals, and pesticides. Therefore, it is crucial to improve the process capability and quality of WSMM by setting optimal parameters. This study proposes a multi-objective optimization methodology based on an intelligent algorithm to optimize the ultra-fine grinding parameters; this can mitigate the issue whereby grinding parameters are difficult to determine during wet grinding industrial production. A mechanistic model is proposed based on the analysis of energy dissipation mechanisms. The specific energy in the WSMM process is quantified using a stressing model. A shuffled frog leaping algorithm (SFLA)-based stressing model is proposed to maximize the specific stress intensity and specific stress number of the entire system under the constraint of the product particle size and grinding time, which provides the optimal process parameters. The performance of the proposed strategy is validated using two case studies in different industrial optimization scenarios. The result of the first case study illustrates that, in comparison to a quadratic programming-based response surface methodology, the proposed SFLA-based stressing model greatly enhances the wet grinding efficiency (decreasing P₈₀ from 3.28 μm to 2.88 μm). In the second case study, the parameter optimization under different feed particle sizes and different productivities was discussed. The results confirmed that the optimized parameters can achieve the minimum particle size (P₅₀ = 1.78 μm) and maximum solid concentration (C_v = 120 g/L) within the minimum grinding time (t_g = 5 min). The contribution of our work lies in the fact that the proposed SFLA-based stressing model can direct multiple-objective decision-making in a more efficient way without requiring costly experimental procedures to acquire the optimized parameters in WSMM. The proposed approach is systematic and robust and can be integrated into WSMM architectures for parameter optimization in other complex wet grinding systems. Full article

Municipal solid waste incineration fly ash (MSWIFA) can be reused as an admixture in cementitious materials, but its low activity limits its utilization as a resource. In this study, we systematically investigated the mineral and grinding characteristics of MSWIFA and then studied its pretreatment and activation via mechanical force–surface modification. The results indicate that the fineness and angle of repose of MSWIFA during grinding are inversely proportional to grinding time, while specific surface area and powder fluidity increase. Agglomeration occurs in the later stage, and particle size fluctuates. Gray correlation analysis shows that MSWIFA powder with a particle size of 16–45 μm contributes most to compressive strength improvement. The composite surface modifier TEA-STPP benefits grinding, shortens ball-milling time, and increases active particle size content, thereby promoting hydration activity. The best process regarding the modifier was determined. MSWIFA and blast furnace slag (BFS) accelerate early hydration of ordinary Portland cement (OPC) and increase its reaction participation, promoting the generation of calcium chloroaluminate (Friedel’s salt) and monosulfate-aluminate phases (SO₄-AFm) and significantly enhancing the hydration of tricalcium aluminate (C₃A) in OPC. Full article

Ultrafine particles, as raw materials for various industries such as construction and environmental protection, are currently obtained through repeated ball milling and multiple classifications, but classification efficiency remains at a low level. Based on the principle of hydrocyclone classification, this paper designs a hydrocyclone with a triple-vortex finder structure that can achieve finer particle size distributions without altering the main structure of the hydrocyclone. The classification performance of the triple-vortex finder hydrocyclone is investigated through numerical analysis and experimental methods, with numerical comparisons made to single-vortex finder and double-vortex finder structures. The results indicate that with an increase in the number of vortex finders, the static pressure and tangential velocity gradually decrease, reducing the likelihood of tangential vortex formation while meeting classification requirements. The axial velocity in the triple-vortex finder structure is significantly reduced, which extends the residence time within the hydrocyclone and facilitates sufficient particle classification. As the number of vortex finders increases, the zero-velocity envelope surface (LZVV) gradually migrates inward, enlarging the external swirling classification space. Through numerical and experimental analyses, it is found that the triple-vortex finder hydrocyclone exhibits the highest classification efficiency, the strongest cutting ability, and the best classification accuracy. Compared to the single-vortex finder structure, the cutting particle size of the triple-vortex finder hydrocyclone decreases by 2.5 µm, and the content of fine particles in the underflow is reduced by 4.36 percentage points, effectively decreasing the fine particle content in the underflow. The quality efficiency improves by 18.85 percentage points compared to the single-vortex finder, while the quantity efficiency shows no significant decline. The obtained data provide a theoretical foundation and data support for the structural design of the new hydrocyclone. Full article

Electron beam melting (EBM) is an additive manufacturing method that enables the manufacturing of metallic parts. EBM-printed parts require post-processing to meet the surface quality and dimensional accuracy requirements. Machining is one approach that is beneficial for achieving these requirements. However, during machining, particles are emitted and can affect the environment and the operator’s health. This study aims to investigate the concentration of particles emitted during the milling of 3D-printed Ti6Al4V alloy produced by EBM. First, the influence of machining speed and cutting fluids, namely flood and minimum quantity lubricant (MQL), on particle emissions was statistically investigated. Then, the standby time required for the operator to safely open the machine door and interact with the machine within the machining area was studied. In this regard, two scenarios were proposed. In the first scenario, the machine door is open immediately after machining, and the operator waits until the particle concentration is acceptable. In the second, the machine door will be opened only when the particle concentration is acceptable. Statistical findings revealed that cutting fluids have a significant impact on particle emissions, exhibiting distinct patterns for both fine and coarse particles. Irrespective of the scenario, MQL results in higher particle concentration peaks and larger particle sizes, and the operator needs a longer standby time before interacting with the machine. For instance, the standby time in MQL is 328% more than that of the flood system. This study provides insight into sustainable manufacturing by taking into account social factors such as worker health and safety. Full article

Against the backdrop of intelligent manufacturing, the ball mill, as a key energy-consuming piece of equipment, requires an accurate perception of its load state, which is crucial for optimizing production efficiency and ensuring operational safety. However, its vibration signals exhibit typical nonlinear and non-stationary characteristics, intertwined with complex noise, posing significant challenges to high-precision identification. A core contradiction exists in existing diagnostic methods: convolution network-based methods excel at capturing local features but overlook global trends, while Transformer-type models, although capable of capturing long-range dependencies, tend to “average out” critical local transient information during modeling. To address this dilemma, this paper proposes a new paradigm for multi-scale feature collaborative perception. This paradigm is implemented through an innovative deep learning architecture—the Residual Block-Swin Transformer Network (RB-SwinT). This architecture subtly achieves hierarchical and in-depth integration of the powerful global context modeling capability of Swin Transformer and the excellent local detail refinement capability of the residual module (ResBlock), enabling synchronous and efficient representation of both the macro trends and micro mutations of signals. On the experimental dataset covering nine types of fine operating conditions, the overall recognition accuracy of the proposed method reaches as high as 96.20%, which is significantly superior to a variety of mainstream models. To further verify the model’s generalization ability, this study was tested on the CWRU public bearing fault dataset, achieving a recognition accuracy of 99.36%, which outperforms various comparative methods such as SAVMD-CNN. This study not only provides a reliable new technical approach for ball mill load identification but also demonstrates its practical application value in indicating critical operating conditions and optimizing production operations through an in-depth analysis of the physical connotations of each load level. More importantly, its “global-local” collaborative modeling concept opens up a promising technical path for processing a broader range of complex industrial time-series data. Full article

The limestone quarrying and processing industry generates huge amounts of waste, with limestone sludge being one of the most prevalent and challenging by-products. This study aims to evaluate the potential of limestone sludge as a sustainable secondary raw material for the mechanochemical synthesis of bioceramics, specifically hydroxyapatite (HA), for high-added-value applications in bone tissue engineering. High-energy milling is innovatively used as the processing route: dry sludge (functioning as the calcium source), a phosphate source, and water were milled with the aim of producing calcium phosphates (in particular, hydroxyapatite) via mechanosynthesis. The industrial sludge was thoroughly analyzed for chemical composition, heavy metals, and mineral phases to ensure suitability for biomedical applications. The mixture of reagents was tailored to comply with Ca/P = 1.67 molar ratio. Milling was carried out at room temperature; the milling velocity was 600 rpm, and milling time ranged from 5 to 650 min. Characterization by XRD, Raman spectroscopy, and SEM confirmed the progressive transformation of calcite into hydroxyapatite through a metastable DCPD intermediate, following logarithmic reaction kinetics. The resulting powders are fine, homogeneous, and phase-pure, demonstrating that mechanosynthesis provides a low-cost and environmentally friendly pathway to convert limestone waste into functional bioceramic materials. This suggests that Moleanos sludge is a viable and sustainable source to produce tailored calcium phosphates and confirms mechanosynthesis as a cost-effective and reliable technology to activate the low-kinetics chemical reactions in the CaCO₃-H₃PO₄–H₂O system. This work highlights a novel circular economy approach for the valorization of industrial limestone sludge, turning a difficult waste stream into a high-value, sustainable resource. Full article

Addressing the issues of surface machining quality and residual stress in small impellers, the outward opening integral small impeller was selected as the key research object, and the main evaluation indicators of the surface quality of the experiment were set as the surface roughness and residual stress. The finite element simulation technology was used to analyze how the process parameters can affect the residual stress and outer surface roughness of the small impeller. After obtaining the results, the genetic algorithm was used to optimize it to obtain the optimal combination of process parameters. The surface roughness is reduced by 34.2%, and the residual stress is reduced by 28.6%, and at the same time, proved the feasibility of the optimization of the process parameters. The numerical control machining test of the small impeller was carried out to verify the feasibility and accuracy of the process parameter optimization. Full article