Segregation control is a challenging yet crucial aspect of bulk material handling processes. The discrete element method (DEM) can offer useful insights into segregation phenomena, provided that reliable models are developed. The main challenge in this regard is finding a good balance between including particle-level details and managing the computational load. This is especially true for industrial applications, where multi-component flows consisting of particles with various irregular shapes and wide size distributions are encountered in huge amounts. In this work, we review the state of the art in DEM modelling of segregation in industrial applications involving the gravity-driven flow of dry, cohesionless granular materials. We start by introducing a novel scientific notation to distinguish between different types of mixtures. Next, we review how parameters for mixture models are determined in the current literature, and how segregation is affected by material, geometric and operational parameters based on these models. Finally, we review existing segregation indices and their applicability to multi-component segregation. We conclude that systematic calibration procedures for segregation models are currently missing in the literature, and realistic models representing multi-component mixtures have not yet been developed. Filling these gaps will pave the way for optimising industrial processes dealing with segregation.
Due to their low cost, high stability and low toxicity, metal oxide nanomaterials are widely used for applications in various fields such as electronics, cosmetics and photocatalysis. There is an increasing demand thereby for nanoparticles with highly defined properties, in particular a narrow particle size distribution and a well-defined morphology. Such products can be obtained under high control via bottom-up synthesis approaches. Although aqueous processes are largely found in literature, they often lead to particles with low crystallinity and broad size distribution. Thus, there has been a growing trend towards the use of non-aqueous and non-hydrolytic synthesis routes. Through variation of the reaction medium and the use of adequate additives, such non-aqueous systems can be tuned to adapt the product properties, and especially to yield anisotropic nanoparticles with peculiar shapes and even complex architectures. Anisotropic particle growth enables the exposure of specific facets of the oxide nanocrystal, leading to extraordinary properties such as enhanced catalytic activity. Thus, there is an increasing demand for anisotropic nanoparticles with tailored morphologies. In this review, the non-aqueous and non-hydrolytic synthesis of anisotropic metal oxide nanoparticles is presented, with a particular focus on the different parameters resulting in anisotropic growth to enable the rational design of specific morphologies. Furthermore, secondary phenomena occurring during anisotropic particle growth, such as oriented attachment mechanisms, will be discussed.
Particle mixing is a fundamental process used in various industries to handle powders, granules, and pellets. Understanding particle mixing is critical for optimizing industrial processes involving particulate systems, making it an important scientific and practical consideration. In this review, the current research progress of experimental and simulation works for widely used tumbler and convective mixers is reviewed, and research gaps are summarized for future investigations. Finally, some new development points of modern particle mixing technologies and topics are mentioned. This paper provides a comprehensive review of the research work of mixers in particulate systems and sheds light on future research in the field of particle mixing.
Rotating discs are usually used as granulators in many industrial processes. The efficiency of the granulation process in this device is directly related to the particle motion behavior in different flow regimes. In this work, the granular flow in a rotating disc was investigated experimentally and numerically. The Discrete Element Method (DEM) was used in the simulations, while Central Composite Designs (CCD) were employed to quantify the effects of DEM input parameters and operating conditions (filling degree (FD), angle of inclination (AI), and rotational speed) on the contacts between particles. The results showed that the particle–wall static friction coefficient had the most significant impact on the studied response. Additionally, the effect of operating variables on the collision force between particles, the angle of departure and particle velocities was successfully investigated, with corresponding DEM simulation predictions. It was also verified that the simulations performed with experimentally measured DEM input parameter values were able to reproduce the flow regimes in the rotating disc.
Polymer selective laser sintering (SLS) is an additive manufacturing technology that involves the melting of a selected area of particles on the powder bed. A 3D component is then printed using layer-by-layer sintering of the powder bed. SLS is considered one of the most promising technologies applicable to a variety of applications, particularly for manufacturing customized design products with high geometric complexity, such as patient-specific designed implants, surgical tools. Currently, only a small number of polymers are available that are suitable for SLS due to the complex multiple physical phenomena involved. Therefore, it is critical to develop new materials in order to fully realize the potential of SLS technology for manufacturing value-added customized products. For a given material, the quality of powder spreading in SLS plays a key role in printing performance and is a precondition for new material development. The aim of this review is to (1) present flowability characterization methods suitable for SLS, (2) examine the influence of powder properties and flowability on laser–material interaction and the quality of the final part, and (3) discuss the methods adopted in the literature to improve the quality of powder spreading.
The Janssen equation is a widely used method for calculating pressures in bulk storage structures. This review explores the historical legacy of Janssen’s equation and its applications in both planar and three-dimensional structures. Our focus is on the limitations of the original formulation of Janssen, extensions made to avoid these deficiencies, and alternative models that have been developed. The motivation behind these modifications is to improve the representation of shear stress within a grain bin in both the horizontal and vertical directions. Modifications to Janssen’s basic assumptions include the vertical-to-horizontal stress ratio (k), the coefficient of friction between the wall and the stored bulk material (μ), internal angle of friction (ϕ), and bulk density (ρ). We also discuss recent developments in pressure theories, which have provided new insights into pressure fields in bulk storage bins. These modern approaches include the continuum elastic theory and microscopic theory. Finally, we discuss recent developments in pressure theories which provide new insights into the storage of bulk solids. Overall, this review provides a comprehensive overview of the Janssen equation and its historical development, limitations, and extensions, as well as recent advancements in pressure theory that offer a more accurate representation of pressure fields in bulk storage structures.
Since the early 2010s, after decades of premature excitement and disillusionment, the field of artificial intelligence (AI) is experiencing exponential growth, with massive real-world applications and high adoption rates both in daily life and in industry. In particle technology, there are already many examples of successful AI applications, for predictive modeling, process control and optimization, fault recognition, even for mechanistic modeling. However, in comparison to its still untapped potential and to other industries, further expansion in adoption rates and, consequently, gains in productivity, efficiency, and cost reduction are still possible. This review article is intended to introduce AI and its application scenarios and provide an overview and examples of current use cases of different aspects and unit operations in particle technology, such as grinding, extrusion, synthesis, characterization, or scale up. In addition, hybrid modeling approaches are presented with examples of the intelligent combination of different methods to reduce data requirements and achieve beneficial synergies. Finally, an outlook for future opportunities is given, depicting promising approaches, currently being in the conception or implementation phase.
Strongly exothermic reactions inherently pose the risk of thermal reactor runaway, which may lead to very high increase in temperature, hot spots and potential catalyst deactivation. For such reaction systems, reactors with excellent heat removal performance are needed. In the case of methanation of CO/CO2-rich gases, full conversion is not possible in a single adiabatic reactor due to the equilibrium limitation, and in large-scale plants, e.g. coal-to-synthetic natural gas (SNG) plants, a series of four and more reactors with intercooling have been realized. To allow for complete conversion within one reactor, the potential of bubbling fluidized bed (BFB) reactors with immersed heat exchangers was investigated in the US and Germany from the 1960s to the 1980s. A Swiss consortium started to expand the concept to small- and medium-scale plants to allow the production of renewable methane from decentral renewable sources such as wood gasification and biogas. During their tests, it could be shown that the catalyst particle movement does not only allow for optimal heat removal—close to isothermal operation and thus little catalyst sintering—but that the catalyst particle movement over the height of the reactor with different concentration zones favors the chemical catalyst stability. This contribution will review the fluid-dynamic studies for BFB reactors with immersed heat exchangers in the last decades comprising studies with pressure fluctuation probes, optical probes, X-ray tomography studies, and particle attrition studies.
Many processes involve solid bowl centrifuges as a solid–liquid separation step, typically used for clarification, thickening, classification, degritting, mechanical dewatering, and screening. In order to operate solid bowl centrifuges safely, with minimum resource consumption and reduced setup times, modeling and optimization are necessary steps. This is a challenge due to the complex process behavior, which can be overcome by developing advanced physical models and process analysis. This review provides an overview of solid bowl centrifuge applications, their modeling, and addresses future optimization potentials through digital tools. The impact of dispersed phase properties such as particle size, shape, surface roughness, structure, composition, and continuous liquid phase is the reason for the lack of generally applicable models. Laboratory-scale batch sedimentation centrifuges are used to predict material behavior and develop material functions describing separation-related properties such as sedimentation, sediment build-up and sediment transport. The combination of material functions and modeling allows accurate simulation of solid bowl centrifuges from laboratory to industrial scale. Since models usually do not cover all influencing variables, there are often deviations between predictions and the real process behavior. Gray-box modeling and on-line or in-situ process analytics are tools to improve centrifuge operation.
Vibro-assisted fluidization of cohesive micro-silica has been studied by means of X-ray imaging, pressure drop measurements, and off-line determination of the agglomerate size. Pressure drop and bed height development could be explained by observable phenomena taking place in the bed; slugging, channeling, fluidization or densification. It was observed that channeling is the main cause of poor fluidization of the micro-silica, resulting in poor gas-solid contact and little internal mixing. Improvement in fluidization upon starting the mechanical vibration was achieved by disrupting the channels. Agglomerate sizes were found to not significantly change during experiments.
Fugitive particulate matter (FPM) refers to a mixture of solid particles and liquid droplets that are released into the air without passing through confined flow equipment. These emissions of FPM can originate from natural processes and anthropogenic activities. FPM emissions are an important source of PM2.5. Precisely measuring the size, concentration, and other properties of such particulate matter is crucial for effectively controlling emission sources and improving air quality. However, compared with particulate matter emission from stationary sources, it is difficult to monitor the FPM effectively owing to its dispersive and irregular emissions. Traditional measuring methods for FPM are based on sampling, which is a point monitoring approach and can be time-consuming. In recent years, several new techniques based on optical principles, image-based processes and low-cost sensors have been developed and applied for FPM measurement, with the advantages of spatial and time resolutions. The current state and future development of FPM measurements are reviewed in this paper.
The scarcity of natural sands has triggered a considerable increase in the consumption of manufactured sands for concrete production. In this regard, the flakiness of the particles and the excess of fines are the main problems that should be addressed when utilizing manufactured sands. The throat classifier is an air classifier designed for the elimination of fine particles (smaller than 75 micrometers) from manufactured sands. The main features of the classifier have been presented in the literature but the mechanism that drives the classification has not been studied in detail. Therefore, this work explores the mechanism of classification of the throat classifier by using CFD-DPM and CFD-DEM simulations. The accuracy and limitations of the methodologies were evaluated by comparing the results against experimental data obtained at pilot scale. The simulations presented fair results in the representation of the airflow and the particle classification inside the throat classifier. Differences between the predictions using the CFD-DPM and the CFD-DEM methodologies under the simulated conditions were found to be negligible. The results of the simulations allowed for a more detailed understanding of the classification mechanism that occurs inside the device and the influence of operational variables on the equipment performance.
Nowadays, the environmental crisis caused by using fossil fuels and CO2 emissions has become a universal concern in people’s life. Photocatalysis is a promising clean technology to produce hydrogen fuel, convert harmful components such as CO2, and degrade pollutants like dyes in water. There are various strategies to improve the efficiency of photocatalysis so that it can be used instead of conventional methods; however, the low efficiency of the process has remained a big drawback. In recent years, high-pressure torsion (HPT), as a severe plastic deformation (SPD) method, has shown extremely high potential as an effective strategy to improve the activity of conventional photocatalysts and synthesize new and highly efficient photocatalysts. This method can successfully improve the activity by increasing the light absorbance, narrowing the bandgap, aligning the band structure, decreasing the electron-hole recombination, and accelerating the electron-hole separation by introducing large lattice strain, oxygen vacancies, nitrogen vacancies, high-pressure phases, heterojunctions, and high-entropy ceramics. This study reviews the recent findings on the improvement of the efficiency of photocatalysts by HPT processing and discusses the parameters that lead to these improvements.
Ultrafine bubbles (bulk nanobubbles), small bubbles less than 1µm in diameter, have attracted academic and industrial attention because of their numerous advantages, including their chemical-free nature and extraordinarily long lifetime. The long lifetime is related to the much higher Brownian motion velocity than buoyancy. The reason why ultrafine bubbles can endure under stable conditions is still unclear, even though their inside is highly pressured. They have several characteristics, such as pH-dependent surface charge and reduction in friction. They are also closely related to ultrasound. Ultrafine bubbles are generated and removed by selecting the ultrasonic frequency. Reaction and separation using ultrasonic cavitation and atomization, respectively, are enhanced by ultrafine bubbles. They can produce hollow nanoparticles, enhance adsorption on activated carbon, and clean solid surfaces. This review discusses the fundamental and ultrasonic characteristics of ultrafine bubbles and their application to particle-related technology: fine particle synthesis, adsorption, desorption, extraction, cleaning, and prevention of fouling.
Several bacterial pathogens contain membrane ligands that facilitate their binding and internalization into human tissues. In this study, lipooligosaccharides (LOS) from the respiratory pathogen non-typeable Haemophilus influenzae (NTHi) were isolated from the bacterial surface and evaluated as a nanoparticle coating material to facilitate uptake into respiratory epithelium. NTHi clinical isolates were screened to select a strain with high binding potential due to their elevated phosphorylcholine content. The association of particles with human bronchial epithelial cells was investigated as a function of particle surface chemistry and incubation time, and the uptake mechanism evaluated via chemical inhibitor and receptor activation studies. A more than two-fold enhancement in particle uptake was achieved by coating the particles with LOS compared to uncoated or gelatin-coated particles, which was further increased by activating the platelet activating factor receptor (PAFR). These findings demonstrate that bacterial-derived LOS ligands can enhance the targeting and binding of nanoparticles to lung epithelial cells.
We have developed a phase retrieval holography system using a single-board computer (SBC) with a graphics processing unit (GPU) for particle size measurement. The system comprises two cameras connected to the SBC with a GPU (Jetson NanoTM, NVIDIA®), a diode-pumped solid-state green laser, and a beam splitter. The GPU enables us to reconstruct holograms in real-time and measure particle size. The system can record the shapes and positions of particles falling in a static flow in a three-dimensional volume as two holograms generating an interference pattern. Two holograms solve the twin image problem that arises because of the lack of phase information using phase retrieval holography. We also present the requirement of this system for experimentally recording and numerically reconstructing holograms of particles. Finally, we compare the particle size distribution obtained by the system to that of conventional two-dimensional image measurement.
The application of semiconductor nanocrystals containing cadmium, lead, selenium and mercury as constituent elements is strictly limited by concerns about environmental pollution and health effects. Nanocrystals free of these toxic elements are being pushed to the forefront of nanocrystal research because of their environmentally friendly advantages and attractive photophysical properties on recent advances in colloidal synthesis, excellent optical properties, optoelectronic device applications, and biological applications of these environmentally friendly fluorescent nanocrystals. In this context, the first topic in this review paper introduces group IV semiconductors. In particular, the unique light emitting properties generated in silicon nanocrystals of diameters smaller than bulk exciton Bohr radius are highlighted. Next the topic turns to the nanocrystals of group III–V semiconductors. After that, attentions are paid to the lead-free perovskite nanocrystals such as tin-based halide perovskite and double perovskite structures. Recent efforts on how to control nanostructures to enhance photoluminescence quantum yields is highlighted for each semiconductor nanocrystal. Finally, the remaining challenges that must be overcome to realize nontoxic optoelectronic devices will be discussed.