Powder Technology最新文献

Through comparing the drying characteristics of the conventional spouted bed (CSB), the spouted bed with a swirling blade (ASB), and the spouted bed with a combination of a swirling blade and a swirling nozzle (ASNSB), the influence of swirling devices on the drying effect was investigated. In the experiments, various operating parameters were changed to comprehensively discuss the differences in drying rate among these spouted beds. Based on the experiments, the drying process of ASNSB was simulated by CFD. The gas-solid two-phase flow and mass transfer during the drying process were systematically analyzed. The experimental results demonstrate that, compared with CSB, the introduction of swirling devices in ASB and ASNSB effectively promotes the flow of particles and increases the drying rate, especially when the filler mass is low and the particle size is large. Furthermore, the drying promotion effect of the ASNSB is more pronounced at low temperatures and high gas flow rates. The simulation results show that in the drying process of ASNSB, mass transfer mainly occurs on both sides of the conical region, and the axial swirling blade changes the direction of gas flow and promotes the mixing of particles.

Polymer powders are limited by particle size, shape, and properties in various applications. Preserving the chemical properties of the material throughout the process is a critical part of developing consistently shaped nanocomposite powders across diverse polymer types. The goal of this study is to demonstrate continuous methods to produce spherical Polystyrene (PS) and zinc oxide (ZnO) nanocomposites. A single-screw extruder is used to melt mix PS pellets with ZnO nanoparticles, followed by pelletization, dry grinding, and spheroidization in a Downer Tower to form spherical polymers. To minimize agglomeration, the process incorporates an ethanol treatment and a final sieving step. The resulting nanocomposite powder exhibits exceptional flowability and well-defined spherical morphology. Additionally, incorporating ZnO nanoparticles enhanced the thermal stability and glass transition temperature of the nanocomposite while reducing particle size distribution. Thermogravimetric analysis and X-ray diffraction confirm the preservation of the chemical structure of the spherical nanocomposite specimens throughout processing.

Conventionally, tetragonality in BaTiO₃ powder is attributed to grain size, disregarding the role of Ba/Ti ratio. However, our study reveals a significant impact of Ba/Ti ratio on tetragonality in BaTiO₃. With an increase in Ba/Ti ratio from 0.990 to 1.010, particle size remains around 200 nm. Tetragonality initially rises from 1.006 to a maximum of 1.0092 at Ba/Ti = 1.000, then decreases to 1.005. Lower tetragonality is associated with Ba or Ti vacancies, using density functional theory (DFT), we analyzed the electron density and lattice distinction in BaTiO₃ powders. Both Ba and Ti vacancies affect lattice distortion, the Ti vacancies leading to more significant lattice expansion and lower tetragonality than Ba vacancies. Using this powder, we fabricated high-density BaTiO₃ ceramics and multi-layer ceramics capacitors (MLCCs) with X7R temperature stability (−55 to 125 °C, ±15% coefficient) and excellent reliability. This strategy has broad implications for tetragonal BaTiO₃ nanopowders and MLCCs development.

This study aims to create an efficient, rapid, and reliable particle collision model utilizing machine learning techniques for granular flow simulations. A simplified surrogate collision model developed in the framework of a Hybrid Euler–Lagrange (HEL) technique was successfully applied to model particle interactions for flows with a low fraction of the granular phase. The precision of the simplified collision model was evaluated using experimental data obtained from the in-house, two-stream particle collision test rig, focusing on solid phase velocity profiles. The implemented model demonstrates strong concordance with the experimental results. The simulations carried out highlight the relation between the simulation time step and the collision rate, which affects the cost of the numerical simulation. The execution time for both the conventional Discrete Element Method (DEM) on a CPU and the streamlined collision HEL model saw a reduction exceeding 70%.

Processes such as threshing and processing determine the subsequent storage, nutritional value, and economic value of maize kernels. The breakage characteristics of maize kernels in threshing and processing machinery are unknown, which greatly limits the need for new developments in low-loss threshing. In this paper, the breakage characteristics of maize kernels under different impact postures and impact velocities were investigated by a combination of FEM and bench tests. The results showed that the breakage evolution rules of kernel breakage modes were the result of stress concentration and energy conversion. The maize kernel with side posture had the smallest critical breakage velocity of 22.5 m/s for breaking into pieces. A breakage probability model for predicting the degree of maize kernel breakage was proposed, and the energy and velocity thresholds provided a method for the digital design of breakage reduction performance of maize threshing machinery and detection performance of impact breakage machinery.

The hybrid modeling strategy which is based on the integration of mechanistic and data-driven models is proposed and applied on fluidized bed granulation processes. The tight combination of empirical process knowledge by means of a mechanistic model and the multivariate data from PAT sensors allowed to gain benefits from both components. The hybrid strategy avoids any discrepancies between model and real process, increases transferability and general applicability of the model, as well as allows a smooth integration of Quality-by-Design principles for oral solid dosage forms.

In this contribution, two illustrative application examples for fluidized bed spray agglomeration and fluidized bed layering or coating processes are presented. It was shown that the proposed strategy can be successfully applied to support process development on different scales. It results in better process understanding, creation of regime maps and might end with the generation of a digital twin.

Chemical mechanical polishing (CMP) is widely used as an ultra-precision machining technology, which determines the final fabrication accuracy of the device. CeO₂ abrasives exhibit excellent CMP performance due to the unique chemical and mechanical properties. With the increasing demand of planarization, how to improve the polishing efficiency of CeO₂ abrasives in the CMP has become one of the hotspots. Over the past decades, extensive efforts have been devoted to develop the CeO₂ abrasives for CMP and great progress has been obtained. Here, we present an overview on the research of CeO₂ abrasives for CMP. This review starts with a brief introduction the research background and related basic concepts of CMP technology and ceria. Emphasis is placed on the development of CeO₂ abrasives, assisted polishing, CMP mechanism, post-CMP cleaning and computational chemistry. Finally, the future development trend of CeO₂ abrasives and the issues that need to be solved are prospected.

This study introduces the application of a spiral structure in pipeline transportation to enhance the pipeline capacity of a slurry shield circulation system. A numerical model of solid–fluid coupling in a pipeline with a double spiral structure was established based on computational fluid dynamics and the discrete element method (CFD–DEM) theory, and its validity was confirmed through comparison with experimental data. The characteristics of the spiral pipeline were evaluated by comparing them with those of a conventional smooth straight pipeline. A multi-objective optimization method for determining the structural parameters of a spiral pipeline in a slurry shield circulation system is proposed using the Kriging surrogate model. The optimized design of a spiral pipeline was demonstrated through a specific project, and the impact of the main structural parameters on the slurry transport characteristics of the pipeline was analyzed. The results indicated that the optimized structural parameters led to an 18.65% increase in the average flow rate of the slurry and a 19.13% reduction in the accumulation of stones in the pipeline.

We simulated the cohesive particle flow in an impeller-based rheometer using Discrete Element Method (DEM), and we focus on the dynamics of particles around the constriction between the blade and its surrounding vessel wall. The results show that mechanical jamming could transiently and intermittently occur in the constriction, but it is limited in a narrow region and short duration. Larger stiffness of particles and lifting flow pattern are more prone to the occurrence of jamming. The scaling law used to speed up the DEM simulation by reducing particle stiffness may fail for particle flow passing through clearance. The mechanical jamming of particles is in low frequency with value <50 Hz and the duration of an individual jamming event is usually <0.04 s. The existence of mechanical jamming is also illustrated by the experiment, where the wear of particle surface is clearly observed with scratches and pits.

Measuring the 3D morphological properties of granular objects such as aggregates is a critical issue in many fields of science and industry, especially when the objects are fragile or hard to sample. For these reasons, non-invasive techniques based on image analysis are being developed. However, most image analysis techniques can only measure 2D properties. This paper presents a new approach based on both image analysis and a 3D stochastic geometric model called VOX-STORM (VOXel-based STOchastic geometRical Model) to estimate 3D morphological properties. By adjusting the parameters of the model, the latter is able to generate populations of objects whose 2D property distributions match those measured by image analysis, and to predict 3D morphological property distributions. The model is based on a dual architecture combining voxelized structure and alpha-shape meshing of the external surface, which makes object generation extremely fast (about 1000 objects in 20 s), while allowing rapid computation of 3D characteristics. The method is validated twice, first on 3D printed aggregates and then on a population of 40,000 synthetic aggregates, with mean errors of less than 2.5% in all cases and less than 1% for 2D properties. It is then applied to two sets of images of latex aggregates captured by a morphogranulometer. The morphological property distributions and fractal dimensions are compared to ground truth in the 2D case and to laser diffraction measurements in the 3D case. The results are also compared with two other recent stochastic geometric models, and the VOX-STORM model outperforms them in all scenarios, as well as in speed of execution, while agreeing with experimental measurements. Finally, directions for future work are suggested.