Classification Of Particles Research Articles

Computational investigation of a novel hydrocyclone for fines bypass reduction

Hydrocylones offer a process intensified environment for rapid, high-throughput and relatively sharp particle classification or phase separation. One of the main performance limitations of hydrocyclone is bypassing of fines to the underflow. To address this limitation, a novel design modification of fitting thin concentric rings to the cylindro-conical section has been proposed. The Computational Fluid Dynamics (CFD) modeling based methodology has been adopted to validate the hypothesis. Predictions using Realizable k-ε, Reynolds Stress Model (RSM), and Large Eddy Simulation Wall Adopting Local Eddy viscosity (LES-WALE) turbulence models has revealed that LES-WALE matched better with the experimental observations. The experimental data included overall performance parameters and detailed observations of axial and tangential velocity profiles across the hydrocyclone. The validated CFD model and the methodology was then used to predict the behavior of the proposed designs. They established that the modifications held promise in reducing the bypass with additional benefits of sharper separation curve and finer D50.

A detection method of elongated and flat aggregate particles based on multi-view shape features with a single camera

Aggregate is an important component of asphalt mixtures, and its shape has a significant influence on the road quality. In this study, a single industrial camera was used to collect images of aggregate particles during falling; then, their morphologies obtained in multiple views were analyzed. Using the equivalent geometric model, four shape characterization parameters—area variety factor, minor diameter variety factor, maximum elongation factor, and Strip-Block area variety factor—were proposed to compose the multi-view shape feature. On this basis, a general regression neural network was adopted to realize the classification of aggregate particles. The results show that the aggregate classification is slightly different when using different equivalent geometric models, while the aggregate shape can be effectively classified. The accuracy of aggregate classification can be improved by fusing parameters from different equivalent models using principal component analysis; another way is through increasing the frame rate of image collection that may increase the number of views. In general, the findings indicate that the proposed detection method can be applied to actual road engineering, which is of great significance to guarantee pavement quality.

Particle Classification through the Analysis of the Forward Scattered Signal in Optical Tweezers.

The ability to select, isolate, and manipulate micron-sized particles or small clusters has made optical tweezers one of the emergent tools for modern biotechnology. In conventional setups, the classification of the trapped specimen is usually achieved through the acquired image, the scattered signal, or additional information such as Raman spectroscopy. In this work, we propose a solution that uses the temporal data signal from the scattering process of the trapping laser, acquired with a quadrant photodetector. Our methodology rests on a pre-processing strategy that combines Fourier transform and principal component analysis to reduce the dimension of the data and perform relevant feature extraction. Testing a wide range of standard machine learning algorithms, it is shown that this methodology allows achieving accuracy performances around 90%, validating the concept of using the temporal dynamics of the scattering signal for the classification task. Achieved with 500 millisecond signals and leveraging on methods of low computational footprint, the results presented pave the way for the deployment of alternative and faster classification methodologies in optical trapping technologies.

Sterile neutrinos existence suggested from LCT covariance

Sterile neutrinos are known to be hypothetical neutrinos which do not interact via the fundamental interactions described within the Standard Model of Particles Physics i.e. electroweak and strong interactions. They are expected to be important for the understanding of the physics beyond the current Standard Model. In the present work, it is shown that the existence of these particles can be suggested from covariance principle using a covariance group formed by Linear Canonical Transformations (LCTs) associated to a pentadimensional pseudo-Euclidian space. It is established that a spin representation of the LCT group gives a particle classification, applicable to the three families of leptons and quarks, which leads to the prediction of the existence of three sterile neutrinos and their antiparticles.

A review of process models for wet fine classification with high frequency screens

Size control of particulate material is key in a mineral processing plant, allowing the downstream process equipment to separate the mineralogical components. Despite being extensively applied in millimetric particle size classification, recent advances in high frequency screens have allowed their application in grinding circuits, leading to production increases and better classification efficiency for the micrometric range. The development of models to simulate these systems is essential to allow estimating possible technological benefits in the plant performance. This study presents a review of models applied to screening processing, focusing on the wet classification of fine particles, and considering their applicability and limitations. The models review is combined with an assessment of their fitness to a high frequency screening trials database. Among the available alternatives, the model developed by Batterham et al. [1] presented the best fit to the dataset, but it has limited simulation capability. Mwale [2], in turn, was considered to offer the best compromise between fit to data and simulation readiness, since it does not demand a difficult sampling campaign and individualizes the process parameters from the model constants. However, a technology gap is still present, which would involve scale-up procedures from small-scale trials to industrial plants, since all the test work was developed using a real scale device.

Automatic shape detection of ice crystals

Clouds have a crucial impact on the energy balance of the Earth-Atmosphere system. They can cool the system by partly reflecting or scattering of the incoming solar radiation (albedo effect); moreover, thermal radiation as emitted from the Earth's surface can be absorbed and partly re-emitted by clouds leading to a warming of the atmosphere (greenhouse effect). The effectiveness of both effects crucially depends on the size and the shape of a cloud's particulate constituents, i.e. liquid water droplets or solid ice crystals. For studying cloud microphysics, in situ measurements on board of aircraft are commonly used. An important class of measurement techniques comprises optical array probes (OAPs) as developed since the 1970s [13]. While water droplets can be assumed as spherical, the shape and size of ice particles are highly variable. The currently used analysis methods to determine the particles’ size from OAP detection do rarely consider shape details or fine structures of ice particles, which may lead to artificial biases in the results.In this paper, we present two new computational analysis methods, combined in an hybrid approach, for an automatic classification of ice particles and water droplets. The first method computes the principal components of a cloud particle and uses them to determine an ellipse, which can then be used to filter for spherical particles. The second method uses convolutional neural networks (CNNs) for the classification of columns and rosettes, respectively. Although we currently only classify these two particle types with CNNs, the presented method can be easily adapted for the classification of other particle types. The particularity of our method is that we use a virtual data set to pre-train the networks, which are then further trained with a smaller amount of manually classified real cloud particles in a fine tuning step. We evaluated our models on a small data set of real cloud particles and in a final field test on OAP image data that was not previously classified. The precision of this field test was better than 81% and ranged up to 98%, demonstrating that the new methods are suitable for providing profound shape classifications of cloud particle images obtained by OAP measurements. All methods we describe in this paper have been implemented in Python and C and are fully open source. Code and documentation are available on Github (https://github.com/lcsgrlch/oap).

Underwater Holographic Sensor for Plankton Studies In Situ including Accompanying Measurements.

The paper presents an underwater holographic sensor to study marine particles—a miniDHC digital holographic camera, which may be used as part of a hydrobiological probe for accompanying (background) measurements. The results of field measurements of plankton are given and interpreted, their verification is performed. Errors of measurements and classification of plankton particles are estimated. MiniDHC allows measurement of the following set of background data, which is confirmed by field tests: plankton concentration, average size and size dispersion of individuals, particle size distribution, including on major taxa, as well as water turbidity and suspension statistics. Version of constructing measuring systems based on modern carriers of operational oceanography for the purpose of ecological diagnostics of the world ocean using autochthonous plankton are discussed. The results of field measurements of plankton using miniDHC as part of a hydrobiological probe are presented and interpreted, and their verification is carried out. The results of comparing the data on the concentration of individual taxa obtained using miniDHC with the data obtained by the traditional method using plankton catching with a net showed a difference of no more than 23%. The article also contains recommendations for expanding the potential of miniDHC, its purpose indicators, and improving metrological characteristics.

Using fine sand shape metrics determined from X-ray microcomputed tomography to illustrate the influence of particle shape on the properties of dispersed mortars

This study follows a first paper written on some three-dimensional geometrical quantities of a selection of sand particles from the United Arab Emirates (UAE), determined using a combination of X-ray microcomputed tomography (μCT) and spherical harmonic analysis, and their correlation to the properties of mortars made with these sands, with no added dispersant. This paper studies other shape metrics of these same sands, the degree of convexity and the Hofman shape entropy, and the influence of particle shape, as judged from these shape parameters, on the water demand and other properties of dispersed mortars. Five sands from different sources were sampled, wet sieved, and classified into six sieve size classes: 20 μm–75 μm, 75 μm–150 μm, 150 μm–300 μm, 300 μm–600 μm, 600 μm to 1.18 mm and 1.18 mm–2.36 mm. The paper discusses the methods of sample preparation, μCT scanning, and the classification of particles based on their shape. The mortars used a dispersant agent enabling experiments to be conducted at low levels of water to cement mass ratios. A cause and effect relation was established between sand particle shape, in the presence of a dispersant, and properties of mortars such as the wet packing density, the workability in the form of slump-flow, and the water demand. A relationship between plastic density, after the peak of packing, the volumetric water to cement mass ratio (vw/c), and workability, as measured by slump-flow, was developed.

A morphological classification of the fat particles found in the urinary sediment of patients with Fabry disease.

The search in the urinary sediment (U-sed) of fat particles with peculiar morphology is a simple and inexpensive tool for the diagnosis of Fabry disease (FD) nephropathy. In this study we investigated the morphology of a high number of such fat particles with the aim to obtain a morphological classification to be used for their identification. Study of the morphology of fat particles in the U-sed of a cohort of FD patients using: bright field plus phase contrast microscopy (BF+PC), polarized light microscopy (POL), and transmission electron microscopy (TEM). Comparison of these results with those obtained for the fat particles seen in the U-sed of a control group (CG) of patients with non-FD glomerulopathies. FD: 18 U-sed from six patients (three samples/patient) were prospectively investigated and 506 fat particles identified. With BF+PC, these were classified in eight morphological categories (seven of which were confirmed by TEM), and with POL in 10 others. CG: eight U-sed from eight patients were investigated and 281 fat particles identified. These fell into four BF+PC morphological categories and into eight POL categories. While some categories were significantly more frequent in FD others were more frequent in the CG. Our study demonstrates that 1. The morphology of fat particles found in the U-sed of FD patients is much wider and complex than that described so far 2. Several significant differences exist in the morphology of such fat particles between FD and CG patients.

Case study: Dust in the housing of laying birds

As dust particles in laying poultry houses can have negative effects on health and on the environment,this study aimed to measure and compare dust concentrations in egg production sheds. The experiment was carried out in three sheds: Californian shed (G1), conventional closed shed with artificial ventilation (G2) and conventional closed shed without artificial ventilation (G3), and the birds housed were in the 22nd week of housing, in Cuité, Paraíba, Brazil.The dust particles were collected by a gravimetric pump, attached to the workers' clothes, at the height of the respiratory zone, being removed at the end of the working day. Using the evaluation methodologies (total or breathable dust) according to NIOSH (1998), weighing was performed with sample concentration calculation, then the particle size was measured by microscopy according to the methodology described by Feret(Santos, 2001), with diameter of function obtained using an optical microscope, and particles classified by size (inhalable <100 μm, thoracic <25 μm and breathable <10 μm).Considering the limit of concentration x exposure time of 3.0 mg/m3, according to ACGIH (2013), the sheds had the following values: G1-0.98 mg/m3, G2-1.45 mg/m3 and G3-1.13 mg/m3. In the classification of particles by size, the highest frequencieswere 83% thoracic, 13% inhalable, and 4% breathable.With the results presented, the implementation of the Respiratory Protection Program - PPR is suggested.

Coastal Remote Sensing: Merging Physical, Chemical, and Biological Data as Tailings Drift onto Buffalo Reef, Lake Superior

On the Keweenaw Peninsula of Lake Superior, two stamp mills (Mohawk and Wolverine) discharged 22.7 million metric tonnes (MMT) of tailings (1901–1932) into the coastal zone off the town of Gay. Migrating along the shoreline, ca. 10 MMT of the tailings dammed stream and river outlets, encroached upon wetlands, and contaminated recreational beaches. A nearly equal amount of tailings moved across bay benthic environments into critical commercial fish spawning and rearing grounds. In the middle of the bay, Buffalo Reef is important for commercial and recreational lake trout and lake whitefish production (ca. 32% of the commercial catch in Keweenaw Bay, 22% along southern Lake Superior). Aerial photographs (1938–2016) and five LiDAR and multispectral over-flights (2008–2016) emphasize: (1) the enormous amounts of tailings moving along the beach; and (2) the bathymetric complexities of an equal amount migrating underwater across the shelf. However, remote sensing studies encounter numerous specific challenges in coastal environments. Here, we utilize a combination of elevation data (LiDAR digital elevation/bathymetry models) and in situ studies to generate a series of physical, chemical, and biological geospatial maps. The maps are designed to help assess the impacts of historical mining on Buffalo Reef. Underwater, sand mixtures have complicated multispectral bottom reflectance substrate classifications. An alternative approach, in situ simple particle classification, keying off distinct sand end members: (1) allows calculation of tailings (stamp sand) percentages; (2) aids indirect and direct assays of copper concentrations; and (3) permits determinations of density effects on benthic macro-invertebrates. The geospatial mapping shows how tailings are moving onto Buffalo Reef, the copper concentrations associated with the tailings, and how both strongly influence the density of benthic communities, providing an excellent example for the International Maritime Organization on how mining may influence coastal reefs. We demonstrate that when large amounts of mine tailings are discharged into coastal environments, temporal and spatial impacts are progressive, and strongly influence resident organisms. Next steps are to utilize a combination of hi-resolution LiDAR and sonar surveys, a fish-monitoring array, and neural network analysis to characterize the geometry of cobble fields where fish are successful or unsuccessful at producing young.

The occurrence and dietary intake related to the presence of microplastics in drinking water in Saudi Arabia.

The implications and health effects of microplastics (MPs) ingestion are still unclear, yet researchers and organizations around the world are increasingly examining the levels of microplastics and nanoplastics in the environment. This study investigated the presence and the quantity of microplastics in bottled and tap water from five regions in Saudi Arabia and estimated the dietary intake of microplastics. Thirty samples of drinking water were collected from the retail markets in Saudi Arabia. The samples included plastic bottled drinking water, 2 glass bottles, and 2 samples of tap water to represent ground water and desalinated seawater. Sample preparation in the laboratory involved a vacuum-assisted filtration with an inorganic filter membrane (0.2-µm pore size). Identification and classification of microplastics particles using Fourier-transform infrared microspectroscopy (FTIR microspectroscopy). The particle size range screened for in this study was 25-500μm. Microplastics were identified in 17 out of 30 samples. The average of the detected microplastic particles was 1.9 pcs/L (lower bound estimate, LB) and 4.7 pcs/L (upper bound estimate, UB), respectively. The most frequently identified plastic type was polyethylene (PE), followed by polystyrene (PS), and polyethylene terephthalate (PET). Given an average recommended water intake of 3.7 and 2.7 L per day for men and women, respectively, the corresponding daily exposure to microplastics would result in 0.1-0.2 pcs/kg bw. The estimate for high water consumers increases to a daily exposure of 1.7-1.9 pcs/kg bw based on the recommended intake for water in hot weather by the WHO. From these results, we conclude that the level of dietary intake of microplastics from drinking water in Saudi Arabia is low, and according to current state of knowledge, microplastics from drinking water do not pose any concern to the consumers in Saudi Arabia.

Abstract P-13: Structural Studies of Insulin Receptor-Related Receptor Ectodomain

Background: The insulin receptor-related receptor (IRR) was originally discovered due to its high homology to the other family members (insulin receptor and insulin-like growth factor 1 receptor). We determined that IRR can be activated by mildly alkaline extracellular media and has typical features of the ligand-receptor interaction, including its specificity and dose-dependence. Since pH-sensitive properties of IRR are determined by its ectodomain; therefore, we chose as an option to study the soluble extracellular domain IRR. Methods: The investigation carried out in Titan Krios 60-300 TEM/STEM (FEI, USA) CryoEM, equipped with direct electron detector Falcon II (FEI, USA) and Cs image corrector (CEOS, Germany), at an accelerating voltage of 300 kV. Data processing and 3D reconstruction were carried out using computing resources of the Federal Collective Usage Center Complex for Simulation and Data Processing for Mega-Science Facilities at NRC “Kurchatov Institute.” Results: The obtained 2D classifications of particles of the ectodomain IRR at a neutral pH form several 3D models. This indicates that the ectodomain has several possible conformations, which is consistent with our previously obtained data using SEC-SAXS and AFM. In the future, additional careful data processing is required, as well as studies of the IRR ectodomain in mildly alkaline pH. Conclusion: In this study, we presented the structural characteristics of the IRR ectodomain obtained by CryoEM. These results are an important step towards understanding the mechanism of functioning of the IRR.

Parallel realistic visualization of particle‐based fluid

AbstractIn this article, an entirely parallel pipeline for creating realistic visualization of particle‐based fluid is presented, which significantly improves surface reconstruction and foam generation performance. Firstly, we develop a spatial hashing grid‐based strategy. Based on this strategy, a novel narrow band surface reconstruction method is presented, which could even discard the detection of surface particles methods to achieve the effect of accurate identification of surface vertices. And a novel narrow band like diffuse particle classification method is proposed, which ensures less memory access and less computation. Furthermore, in order to obtain a best performance, we put forward several parallel schemes to avoid potential race conditions and invalid threads. Experiments are conducted to verify that our developed method and code efficiently improve the particle‐based visualization.

Measurement of particle agglomeration and aggregate breakdown of reclaimed asphalt pavement

Cold milling of old asphalt pavement is widespread in pavement resurfacing. It produces reclaimed asphalt pavement (RAP) and changes the state of the old asphalt pavement; the pavement is cut into particles, known as RAP particles. RAP particles are composed of many smaller aggregates bonded by the aged binder (particle agglomeration). The aggregates contained in the old pavement can be crushed into smaller pieces in the milling operation (aggregate breakdown). Particle agglomeration increases the risk of insufficient moisture stability and fatigue resistance of the recycled asphalt mixture; aggregate breakdown often prevents the use of a higher RAP content in the new mixture. In this study, the effect of these two problems is studied through a pavement maintenance project. The effect of milling speed on the RAP particle agglomeration rate is evaluated. The size-susceptibility of the agglomerate d particles, gradation deviation of the recycled mixture, and morphology classification of RAP particles are discussed. The effect of milling speed on aggregate breakdown is also investigated. The results show that approximately 30%–50% of RAP particles are made of aggregates smaller than the corresponding particle size. The agglomeration rate increases with an increase in milling speed and RAP particle size. High milling speed produces a lower rate of aggregate breakdown, resulting in a higher coarse aggregate content and a lower filler and fine aggregate content than a low milling speed.

Study on the classification and floc-flotation process of Huangling coal slime

ABSTRACT This study primarily investigates the effect of particle classification on flotation of Huangling (HL) coal slimes with aiming to recover more combustible and clean fuels. The ash composition of fresh slime was analyzed using XRD and ICP-AES. The results showed that HL coal slime had high ash content and difficult washability. Conventional flotation and floc-flotation tests of coarser fraction (0.500 − 0.125 mm) and finer fraction (−0.125 mm) were performed to investigate comprehensively the flotation parameters. Results indicated that particles classification could effectively improved the flotation capacity of coarser fraction. Conventional flotation was suitable for coarser fraction, whereas floc-flotation was suitable for finer fraction. However, finer fraction has a large specific surface area, poor attachment with the air bubbles and entrained a large amount of impurity mineral particles. These features caused conventional flotation ineffective in finer fraction, whereas the addition of Polyacrylamide (PAM) anionic flocculant significantly enhanced flotation recovery. The excessive flocculant could not be continuously improved flotation effect, possibly because the excessive PAM molecules were adsorbed on the surface of coal particles, which accelerated the rupture of liquid film between coal particles and bubbles. Furthermore, the floc-flotation could effectively increased the particle size and reduced specific surface area of finer fraction.

The New Paradox of Dual Modality X-Ray Diamond Sorting

Modern-day diamond sorting is achieved through the application of X-ray luminescence (XRL) and X-ray transmission (XRT) techniques. Sorting with XRL is limited to the class range of 1.25 mm to 32 mm because of self-absorption associated with larger diamonds, greater than 32mm. The effect of self-absorption is also a high-energy phenomenon in XRL. XRT is limited to sorting large size diamonds as the technique suffers poor contrast for diamonds smaller than 10mm. XRT measurements are immune to self-absorption for all sample sizes, while XRL measurements have good contrast for particles smaller than 32mm. The applications of these techniques have hitherto been used independently of each other and have subsequently progressed mutually exclusively. Here we analytically show a new paradox of a dual-modality X-ray diamond sorting combining XRL and XRT techniques' strengths. Key features of our new paradoxical model performance are contrast mitigation for small particles and self-absorption rejection for a large particle at high energy as well as improved particle detectability and classification.

Particle classification characteristics of the particle classifier of decoupled dual loop reaction system

A particle classifier consisting of a cyclone separator and a settling separator for a decoupled dual loop reaction (DDLR) system has been designed. The classifier is expected to separate the circulating bed materials into two functionalized groups of different particle sizes and/or densities and allocate them into the pyrolysis/gasification loop and the upgrading/reforming loop of the DDLR system individually, so that the reactions in the dual loops could independently occur under optimized thermodynamic and kinetic conditions. To this purpose, the flow field and particle classification performance of the classifier have been investigated by means of numerical simulation and cold experiment with quartz sand and/or coal of different particle sizes as feedstock and air as driving gas. The simulation results show that the cyclone separation and the settling separation are interactive and their contributions to the flow field could be regulated individually to realize an efficient classification of mixed particles with adjustable cut-size. The experiments show that the settling separation takes major role in the classification performance, which is determined by the particle terminal velocity. The mixture of binary particle groups with different particle sizes and/or densities could be quickly and efficiently separated with a Newton efficiency up to 98%.

Effect of Sawdust Particle Size on Physical, Mechanical, and Energetic Properties of Pinus durangensis Briquettes

Particle size is a physical property that sometimes limits the quality of briquettes, so it is recommended to use different sizes in mixtures for their manufacture. The objective of this research was to evaluate the effect of different particle sizes of sawdust in mixtures on some physical, mechanical, and energetic properties of briquettes made from Pinus durangensis sawdust, as well as set the ranges within the appropriate values found to obtain desired values. Three particle sizes were established (large, medium, and small), and 10 mixtures were prepared using different percentages of each particle classification. The particle density, volumetric swelling, compressive strength, impact resistance index (IRI), and gross calorific value of the briquettes were evaluated. For the determination of optimal mixtures, the surface response methodology was used under a three-factor simplex-lattice model. The particle density values were in the range 0.92 to 1.02 g cm−3 and the volumetric swelling was 0.96 to 3.9%. The highest resistance to compression was 37.01 N mm−1, and the IRI was found in the range of 53 to 107%. The gross calorific values were from 19.35 to 21.63 MJ kg−1. The selection of different particle sizes for the mixtures increases the quality of the briquettes.

FFWR-Net: A feature fusion wear particle recognition network for wear particle classification

Wear particles produced by machines in the process of wear carry valuable information including wear mechanism and wear severity. Wear particle classification based on wear particle images provides predictive analysis for wear condition of machines. A novel wear particle recognition network based on feature fusion, FFWR-Net, is proposed in this research paper for wear particle images classification. In FFWR-Net, traditional feature extraction method by image processing technique (i.e. manually feature extracting) and deep learning convolutional neural network method (i.e. automatically feature extracting) is paralleled to extract the features of wear particle image. Then the features obtained by two different methods are fused together for building a wear particle classifier. In order to verify the effectiveness of the proposed classifier, it is compared with the previous convolutional neural network models on the same wear particle dataset. The comparison results show the accuracy and effectiveness of the proposed FFWR-Net classifier is better than the previous models.