Elutriation Research Articles

Research and Optimization of Operating Parameters of a Rotor Classifier for Calcined Petroleum Coke

This article explores the impact of operating parameters on the classification efficiency of a rotor classifier. Based on the experimental data of calcined petroleum coke classification, a single-factor experimental analysis is conducted to find the relationship between operating parameters and classification performance. The cut size becomes progressively smaller as the rotor speed and feeding speed increase, and progressively larger as the inlet air volume increases. Newton’s classification efficiency and classification accuracy decreased with the increase in feeding speed. The range analysis of the orthogonal experiment shows that the rotor speed and inlet air volume have significant effects on the classification performance, but the effect of feed speed is relatively weak. In addition, the optimal combination of operating parameters is obtained by optimizing the operating parameters. Newton’s classification efficiency under this combination is estimated, and the estimated value is 82%. The verification experiment reveals that the Newton’s classification efficiency is 83.5%, which is close to the estimated value. Meanwhile, the classification accuracy is 0.626. This study provides theoretical guidance for the industrial production of calcined petroleum coke and accumulates basic experimental data for the development of air classifiers.

Dry fractionation of Australian mungbean for sustainable production of value‐added protein concentrate ingredients

AbstractBackground and ObjectivesDry fractionation, combining milling and air classification technology, offers an economical and sustainable pathway for the processing of value‐added protein ingredients from Australia's major pulse crops. Leveraging a wider range of pulses will help meet the growing demand for plant‐based protein ingredients. This study demonstrates the efficient processing of protein concentrates from Australian mungbean, which were characterized for key nutritional properties, phytochemical constituents, protein quality, and protein secondary structure.FindingsMilling and air classification of mungbean flour resulted in protein concentrates (fine fraction) with a protein content of 62.2 g/100 g (db), reflecting a two‐fold increase in protein content compared to the original flour (p < .05). Ash, fat, dietary fiber, and free phenolic and antioxidant compounds, also co‐concentrated with protein in the fine fraction (p < .05), imparting enhanced nutritional properties with the potential to deliver health benefits. The protein quality of dry fractionated mungbean protein concentrate was compared to a commercial source of mungbean protein isolate (88 g/100 g, [db]). Amino acid scores highlighted deficiencies in sulfur‐containing amino acids, methionine, and cysteine, as well as tryptophan and threonine. In vitro protein digestibility‐corrected amino acid scores for the mungbean protein concentrate and protein isolate were 63.7% and 59.5%, respectively. SDS‐PAGE protein profiles showed no major differences in protein composition, indicating that no specific types of proteins are lost during the dry fractionation process. Fourier transform infrared analysis of protein secondary structure showed that β‐sheets were the dominant structural component, with relative percentages of 36% and 49%, for the mungbean protein concentrate and isolate, respectively.ConclusionsThere is significant potential for dry fractionation to deliver value‐added opportunities for Australian mungbean, through the production of protein concentrate ingredients with enhanced nutritional properties for use in new and existing foods.Significance and NoveltyNutritional properties, protein quality, and protein secondary structure were compared for protein concentrates and protein isolates processed from Australian‐grown mungbean. Key findings from this study will inform pulse protein processors and pulse‐based food manufacturers.

Pilot scale air classification of flours from hulled and hull-less barley for the production of protein enriched ingredients

This study explored pilot scale air classification of flours from two barley varieties: CDC Austenson (hulled) and CDC Valdres (hull-less) for the production of protein enriched food ingredients. The objective was to understand the effect of repeat milling and air classification cycles on the shift of proteins and other components including starch, β-glucan, fiber and ash. Protein enrichment was achieved for both varieties, with 2.13 and 1.75 times increase in protein content for CDC Austenson and CDC Valdres, respectively, after the first fractionation cycle. Repeat cycles led to further protein separation for both varieties, resulting in an effective protein separation efficiency of 47–52% w/w towards the fine fractions. The fine fractions were also enriched with fat and ash. Whereas, the coarse fractions were enriched in total, soluble and insoluble dietary fibers as well as β-glucan. Starch distribution varied between varieties, with CDC Austenson showing a shift towards coarse fractions, while towards fine fraction for CDC Valdres due to its smaller granule size. This study established a pilot scale process for fractionation of barley. The compositional data of these fractions will aid the development and use of barley as a food ingredient in foods including snacks (starch-rich) and meat alternatives (protein-rich).

Modelling the Sorting of Lithium-Ion Battery Components in a Zig-Zag Air Classifier

The recycling of lithium-ion batteries, in particular, has become increasingly important in recent years. Due to the materials contained, such as copper or nickel, the return to the economic cycle is important. To ensure this, binding measures have been introduced by the European Commission. As part of the mechanical recycling of lithium-ion batteries, the zig-zag air classifier is used to separate battery components. One application is the separation of the current conductor foils from each other, which is investigated and modelled here. Existing models deriving from the literature are evaluated for material fractions coming from the recycling of different automotive lithium-ion batteries. Since the separation depends on the geometry of the foil particles, similarities for separation depending on the geometric characteristics of the electrodes are derived. It turns out that the material is too complex for the empirical model. However, the model can be used to evaluate the suitability of the apparatus and the quality of the separation.

Research: influence of air classification ratio on low nitrogen performance of natural gas burner

ABSTRACT An innovative low-nitrogen natural gas burner was developed, leveraging air-staged combustion as the primary mechanism, supplemented by swirl combustion and flue gas recirculation technology. This design enables adaptation to a multitude of combustion conditions through the adjustment of the secondary air ratio. A prototype burner with a power output of 60 kW was constructed for experimental study. The velocity field, gas concentration field, temperature field, and NO x concentration within the combustion zone were analyzed under different load, and the impact of the secondary air ratio on the burner’s low-nitrogen combustion performance was explored. Experimental results showed that the burner can maintain stable combustion and low NO x emissions and had good load adaptability. The introduction of secondary air resulted in a “W”-shaped distribution characteristic within the combustion zone’s velocity and concentration fields, and effectively suppressed the formation of high-temperature zones, thereby reducing the generation of thermal NO x . A secondary air ratio of 27% significantly enhanced the gas atmosphere within the combustion zone, leading to more uniform gas distribution and improved mixing of the gas mixture. Furthermore, it had a better low-nitrogen performance. With an increase in burner load, the NO x concentration within the combustion zone also increased, reaching a peak value of 31 mg·m−3 at full-load, which still complied with low-nitrogen emission requirements.

Classification Evaluation Method Considering the Effect of Specific Gravity Sorting in Air Classification—Case Study of Solar Panel Cell Sheets Classification—

Classification Evaluation Method Considering the Effect of Specific Gravity Sorting in Air Classification—Case Study of Solar Panel Cell Sheets Classification—

Simulation of the Classification of Manufactured Sands in the Throat Air Classifier

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.

Influence of guide vane on dispersion of aggregates near the guide vane in a turbo air classifier

Ultra-fine particles are prone to agglomeration, limiting the preparation of ultra-fine powder. The aggregates' disaggregation mechanism is studied, and the proposal of improving the guide vane of a turbo air classifier is contributed to strengthen powder disaggregation based on CFD-DEM coupling and the novel local fluid domain extraction method. The results show the cylindrical tail of guide vane is conducive to the disaggregation, and the powder dispersion near the guide vane is improved. When the inlet air velocity vin is <18 m·s−1, the cylindrical tail of guide vane can significantly improve the powder dispersion. When the cylinder diameter is larger than or equal to 2 mm, the effect of the increase of the cylinder diameter on powder dispersion is significant. However, the increase of cylinder diameter will lead to the increase of energy consumption, so the cylinder diameter should not be too large.

Computational fluid dynamics simulation and performance study of a three-separation combined air classifier

This paper mainly uses ANSYS-FLUENT 19.2 software to simulate the movement of the air-flow in a three-separation combined classifier. The simulation results indicate that the air-flow is uniformly distributed in the V-classifier under the action of dispersion plates and baffles. As the air-flow enters the rotor channel, the tangential velocity of the air-flow increases uniformly and remains stable in the axial direction, providing a stable centrifugal force field for particle classification. From the analysis of the flow field and particle trajectories, the separation interface between the upward path for particles and the downward path for coarse particles is relatively clear. The experimental results with sodium bicarbonate show that the V-classifier has a good pre-classification effect. The rotor cage speeds of 300 rpm and 500 rpm are the best working conditions for the coarse powder and fine powder collection, respectively. This study not only provides a new strategy for the design and development of air classifier, but also provides theoretical guidance for its application in industrial production.

Evaluation of a Process to Separate Ensiled Corn Grain and Stover for Improved Utilization as a Biomass Feedstock

Highlights An experimental machine that combined air classification and mechanical screening was investigated to separate corn grain and stover. Important variables included stover:grain ratio, air velocity, and throughput. As much as 99.4% of the grain could be separated from the stover with less than 1% foreign matter in the grain. Abstract. Corn grain and chopped stover were stored anaerobically at 37% to 50% (w.b.) aggregate moisture content to create a unique biomass feedstock. However, after storage, the two fractions must be separated to accommodate different conversion pathways. In this work, a modified cleaning system from a grain combine harvester was evaluated to investigate its effectiveness in separating the grain from the stover. While this system has been purposefully built for this task, there is no previous literature on separating grain at high material other than grain (MOG) to grain ratios. Using this system, material was separated into four fractions: Grain, Tailings, Heavy MOG, and Light MOG. Subsamples were collected, oven dried, and then hydrodynamically separated to quantify the grain content in the four fractions. Several different configurations of air velocities, mass flow rate, material MOG: grain ratios, and sieve types and openings were investigated. Grain capture was defined as the fraction of the total grain dry mass collected in the grain and tailings fractions. In five experiments, the grain capture effectiveness varied from 89.2% to 99.4% on a dry basis. The dry basis foreign matter (FM) in the clean grain was 0.6% to 10.8%. The use of a modified combine cleaning system has the potential to be one step in a system to fractionate corn grain from stover (i.e., MOG) in a biorefinery that uses both starch and cellulose as biomass feedstocks. Keywords: Air classification, Anatomical fractionation, Cleaning shoe, Corn stover, Mechanical sieving.

Impact of entrance angle of rectangular openings of centrifugal multivortex device on its classification efficiency

Air classification is a common process in many industries. The paper presents an analysis of the operation of a centrifugal multivortex classifier designed to separate solid particles. The classification process of gas-solid system is observed between the outer pipe and the inner pipe, which has rectangular openings that provide the formation of the stable vortex system. The purpose of this work is to study the influence of the entrance angle of rectangular openings on the classification effectiveness of the device. The results show that the maximum effectiveness in the classification of particles larger than 40 μm is achieved at an angle of 0.8 and a gas flow speed of 16 m/s, providing an effectiveness of 69.6%. Increasing the speed at the inlet to the device improves the effectiveness, since the vortex structure in the interpipe space becomes more stable. It is recommended to select the classifier with the largest entrance angle of rectangular openings, since the lowest pressure loss is achieved. Changing the entrance angle of rectangular openings of the device has practically no effect on the effectiveness of particle classification.

Influence of secondary air on the flow field of NGZF classifier

Aiming at the problems of uneven material dispersion of traditional air classifier, NGZF classifier with downward air inlet fabric method is designed. In order to investigate the classification law of the classifier and the effect of secondary air on the flow field of the classifier, this paper utilizes ANSYS-Fluent19.2 software to simulate the internal flow field and conducts material experiments using the response surface method. The simulation results demonstrate that with the rise of the secondary air volume, the overall flow field distribution tends to be appropriate, and the washing flow field created by the secondary air does not interfere with the primary flow field. However, when the secondary air is too big, it will generate turbulence in the axial velocity in the guide cone zone. The discrete phase model (DPM) indicated that the secondary air would re-screen the misclassified fine particles. Response surface experiments with calcined petroleum coke showed that by setting the cut particle size at 50 ?m and maximizing the classification accuracy, the ideal operating parameters were a rotor speed of 1037.4 rpm, a primary air volume of 429.6 m3?h-1, and a secondary air volume of 50.2 m3?h-1. This work not only provides a novel approach for the design and development of airflow classifiers but also gives theoretical direction for their implementation in industrial production.

Моделирование аэродинамики закрученного турбулентного потока в воздушно-центробежном классификаторе

Centrifugal air classifiers are the promising devices for fractional separation of powders by size. Large particles of a powder material are thrown to the periphery due to the predominance of centrifugal forces over aerodynamic forces. This paper studies the aerodynamics of a swirling turbulent flow for different working area designs of the classifier. Turbulence is simulated using the RANS method and the Wilcox turbulence model. To determine the efficiency of the classification of the powder material and to obtain a separation curve using the Tromp method, two calculation methods, i.e., trajectory and probabilistic methods, are proposed. The obtained results are validated with experimental data and through various test studies. Analysis of the numerical study of fine powder classification has shown the viability of the proposed geometry for a separation chamber in comparison with the traditional geometry. The most optimal mode of aerodynamic characteristics of the centrifugal classifier is revealed to obtain the best sharpness of particle size separation.

Distribution of Heterogeneous, Highly Impure Soy Material in a Deep Air Channel

The paper highlights a lack of machines and equipment capable of cleaning highly impure soybean material efficiently and productively. Most of the existing grain cleaning machines are designed for the materials conforming to GOST 17109-88 that specifies weed and oilseed impurity contents of 2 and 6 percent, respectively. In reality, a typical soybean post-harvest waste often contains 20 to 60 percent of material unsuitable for extracting raw protein, that makes this type of material completely non–separable for this type of machine. (Research purpose) The research aims to determine the optimal efficiency of separating heterogeneous bulk mixtures in a vertically ascending air channel. (Materials and methods) To accomplish this, the authors employed a rotary batch classifier RBK 30 and a prototype sample of a precision air classifier PAC with column air flow accelerators. The paper defines impurity separation completeness and soybean waste separation efficiency in a novel pneumatic classifier, considering the accelerator thickness and the height above the processed material. (Results and discussion) The findings reveal that traditional pneumatic channels achieve no more than 20 percent of separation efficiency for highly impure soybean material, while the precision air classifier demonstrates approximately a 45 percent separation. (Conclusions) Typical air flow machines struggle with high impurity of material. The separation efficiency of highly impure material can be enhanced by equalizing the air flow velocity inside and above the layer of the separated material and installing a core air flow accelerator. The study indicates that the material being processed can be separated by air flow, provided a specialized pneumatic separation channel is used. Due to the ratio of the core accelerator height and the height above the material, the air flow velocity inside and above the separated layer is equalized.

Effect of relative humidity on milling and air classification explained by particle dispersion and flowability

Effect of relative humidity on milling and air classification explained by particle dispersion and flowability

The influence of air inlet layout on the inner flow field for a vertical turbo air classifier

In this study, the influence of air inlet layout on the flow field distribution and particle movement trajectory for the vertical turbo air classifier are analyzed comparatively using the numerical simulation method. The air inlet layout adjustment can increase the axial velocity and turbulent dissipation rate at the feeding inlet and do not generate the axial negative velocity, which improves powder material pneumatic transportation and dispersion capacity; the air inlet layout adjustment can match the airflow rotation direction with the rotation direction of the rotor cage, which can eliminate the vortices in the rotor cage channel effectively. Moreover, the particle movement time is shortened and fast classification is completed, which can decrease the particle agglomeration probability and weaken the ‘fish-hook’ effect. The optimization scheme of the air inlet layout is Type-BC. In accordance with the numerical simulation results, the calcium carbonate classification experimental results indicate that the classification performance of the classifier is improved using Type-BC.

Influence of the Cell Type on the Physical Processes of the Mechanical Recycling of Automotive Lithium-Ion Batteries

Lithium-Ion Battery (LIB) manufacturers produce different cell formats (prismatic, cylindrical, pouch, etc.) with different casing materials (steel or aluminium) and cell chemistries (e.g., NMC, NCA, LFP, etc.) for application in electric vehicles. By law, these cells have to be recycled after their lifetime. This study investigates the influence of different cell types on the outcome of a standardized mechanical recycling process consisting of crushing, sieving and air classification. The aim of the study is to find out whether different cell types can be processed together or whether the recovery and product quality can be improved by processing them separately. Pouch cells require low energy consumption for crushing compared to cylindrical and prismatic cells. Steel as a casing material increases the energy requirement during crushing compared to aluminium. The particle size distribution of several product fractions varies significantly between the different cell types. During air classification, the separator, anode, and cathode show a similar separation behaviour and can be processed with the same settings, whereas for the separation of the casing metals, different settling velocities need to be applied depending on the casing material.

Air Pollution Monitoring Approach using Atomic Orbital Search Algorithm with Deep Learning Driven

&lt;p&gt;Air pollution is a major reason for health-related issues and weather changes, one of humanity's most dangerous problems. It is the most crucial environmental issue in the 21st century and has attracted global attention. These challenges are exacerbated by an overabundance of automobiles, industrial output pollution, transportation fuel consumption, and energy generation. Therefore, air pollution prediction was developed vital. Air pollution monitoring is the procedure of analyzing and measuring the air quality in a certain place to develop the levels of several pollutants and contaminants present. Monitoring air pollution is vital to understand its sources, and effects on the environment and human health, and for executing methods for mitigating its effects. Deep learning (DL) approaches are employed for air pollution predicting methods. Therefore, this study develops an Atomic Orbital Search Algorithm with a Deep Learning-Driven Air Pollution Monitoring (AOSADL-APM) approach. The purpose of the AOSADL-APM technique is to predict and classify the presence of air pollutants. In the presented AOSADL-APM technique, the min-max normalization approach is applied for data pre-processing. For air pollution prediction and classification, the AOSADL-APM technique applies deep long short-term memory (DLSTM) methodology. To enhance the performance of the AOSADL-APM technique, the AOSA-based hyperparameter tuning has been developed. The simulation results of the AOSADL-APM technique were tested using the benchmark dataset. The widespread outcome analyzed the greater solution of the AOSADL-APM algorithm compared to existing approaches.&lt;/p&gt;&#x0D;

CFD simulation and optimization of an industrial cement gas–solid air classifier

An air classifier is one of the main and effective devices in cement industry. In this study, a three-dimensional, steady and two-phase (solid-gas) computational fluid dynamics (CFD) simulation was performed to optimize the performance of this device in the Kerman Momtazan cement plant, Iran. After the validation of CFD results, the air flow field and air path lines between fixed blades were checked carefully and the non-uniformity in velocity distribution and the formation of vortex flows between the blades close to particle inlets were observed. The study tried to improve the device efficiency by changing the method of entering particles into the device, resulting in a reduction in air classifier electrical energy consumption (from 41 to 35 (kW h)/t) and an increase in production rate (from 203 to 214 t/h). Additionally, the study investigated the effects of other modifiable operating conditions like rotary cage rotation speed, pressure difference, and inlet air temperature on the particle size distribution and classifier efficiency. The results showed that increasing the cage rotation speed decreased the product rate and the product particles mean diameter while increasing pressure difference or increasing temperature increased the product rate and the product particles mean diameter. It was concluded that these modifiable operating conditions can significantly affect the performance of the air classifier in the cement industry.

Dry fractionation for endosperm recovery from a barley malt waste stream

Cereal processing industry removes the fibrous tissues from kernels via abrasive milling, but this tends to remove part of the valuable endosperm components as well. Therefore, endosperm recovery from such abraded barley malt material (53% endosperm and 47% husk) was evaluated for three dry separation technologies. Electrostatic separation recovered 25% of the endosperm at 85% purity and this recovery increased to 39% in a second run, which indicated potential to further improve the setup. Increasingly finer sieves removed up to 40% of the husk with little endosperm loss, but further husk removal up to 95% by the finest sieve reduced the endosperm yield to 54% due to decreasing differences in smallest diameter between the endosperm and husk particles. Air classification outperformed sieving by yielding 71% of the endosperm while removing 95% of the husk, and further, less selective air classification steps could yield up to 94% of the endosperm while still removing 59% of the husk. Moreover, such additional air classification steps currently recovered residual endosperm particles more selectively than electrostatic separation after an initial air classification. Overall, air classification after abrasive milling increased the removal of insoluble matter from malted barley kernels. The loss of soluble endosperm components remained similar to the loss as observed in a single abrasive milling step.