Air Classification of Powdered Materials

Air classification of powdered materials offers a way to control particle size distribution in solids-handling operations. Information on key performance metrics and operating practices of size classification equipment is presented here

The handling and processing of solid particulate materials are fundamental to a vast array of industries, ranging from food and pharmaceuticals to minerals and chemicals. Particle characteristics, and their size distribution within a powder or bulk solid, profoundly impact material properties, process efficiency and final-product quality. Particle size distribution (PSD) is characterized by the quantity — for instance, the mass of particles present — in individual size classes. PSD influences flowability, reactivity, surface area, packing density, appearance and even taste and texture in food applications. Consequently, accurately controlling PSD is a critical aspect in solids processes and demands meticulous attention.

Particle classification is the process of fractionating a solid particulate material, which holds a range of different particle sizes, into two or more fractions, each targeting specific particle size ranges, based on some physical property, most commonly size or density. Classification methods can be broadly categorized as wet or dry. Dry classification, particularly air classification, is favored in situations when dry powders are involved, when the material reacts adversely with liquids, or when drying the product afterwards is energy-intensive. This article provides an overview of the working principles of rotary air classifiers and outlines their key performance metrics in practical applications. It highlights their essential role in enabling the efficient and sustainable management of particulate solids.

Particle size

To understand size classification of powdered materials, it is essential to first grasp how particle size is defined and represented. Individual elements of particulate matter form distributions that can be characterized by the quantity (for example, number or mass) of particles present in individual size classes. Figure 1 shows a cumulative PSD, where the horizontal axis indicates particle size on a logarithmic scale and the vertical axis shows the cumulative percentage of particles smaller than each size. Common indicators include D₅₀, which represents the particle size below which 50% of the sample lies, shown by red lines in Figure 1. Similarly, D₁₀ and D₉₀ correspond to the sizes below which 10% and 90% of the particles fall, shown by green and blue lines in Figure 1, respectively.

It is noted that the size of an individual particle is often represented by an equivalent diameter, which might be based on geometry or hydrodynamic or aerodynamic behavior. A variety of methods, such as sieve analysis and laser diffraction, each with its own limitations, can be used to analyze particle size across different size ranges. These methods often yield different magnitudes of equivalent diameters because they are based on different physical principles and use various measures of size-related physical quantities. The resulting data are then compiled into statistical representations, such as the graph shown in Figure 1.

Classifiers are devices designed to separate feed material into at least two distinct fractions, as shown in Figure 2 — a coarse fraction and a fine fraction. This size separation is primarily characterized by a parameter known as the cut size, which is the particle size at which the material is expected to be ideally divided. Particles smaller than the cut size are directed to the fine fraction, while larger particles are collected in the coarse fraction. For instance, in Figure 2, the feed material, represented by the black curve with a D₅₀ of 10µm, is separated at a cut size of approximately 20µm, as indicated by the red vertical line. Ideally, the fine- and coarse-fraction curves, shown as dark green and dark blue, respectively, should be positioned on either side of the cut size. In practice, however, a sharp separation is rarely achieved. As a result, actual PSDs of the fine and coarse fractions often deviate from the ideal curves and instead resemble the light green and light blue curves, respectively, in Figure 2. The degree of overlap between these curves with respect to the target cut size, shown as the hatched areas below the cut size in the coarse fraction and above the cut size in the fine fraction, reflects the achieved sharpness of the cut. Along with the sharpness of the cut size, the mass ratio of the separated fractions, determines the overall effectiveness of the classification process.

Rotary-air classifier mechanics

Air classification is a technique used to separate particulate materials according to their aerodynamic properties, size and density. The principle of this technique dates back to early manual winnowing practices, where natural wind currents were used to separate lighter materials, like chaff, from heavier grains. Figure 3a illustrates a modern industrial air classifier and Figure 3b provides a schematic representation of its operating principles within the classifier chamber.

The movement of a particle in an air stream is governed by the interplay of gravity-induced and aerodynamic-induced forces. For each particle, gravity acts as a constant downward force proportional to its mass. The drag force, which opposes the particle’s motion relative to the air, is influenced by the particle’s size and shape, as well as by the velocity of the air stream. This force tends to carry the particle in the direction of the airstream. Smaller or less dense particles experience a relatively larger drag force compared to their mass.

The rotational flow of the air generates a centrifugal acceleration field, which acts radially outward on the particles. In the presence of a centrifugal acceleration field, particles moving within this field experience an outward centrifugal force. This force is proportional to the particle’s mass (and thus roughly its volume or density times the cube of its size) and the square of the rotational speed of the air. Larger or denser particles experience a relatively stronger centrifugal force.

Simple winnowing relies mainly on the combined effects of drag and gravity in a unidirectional airstream. In contrast, modern air classifiers use specially designed chambers to create a circulating downward airflow that carries the particles. In static systems, such as cyclones, this airflow eventually forms a central upward vortex through which the air exits. In dynamic systems, like rotary classifiers, the airflow passes through a fast-spinning slotted drum, allowing for a much sharper cut than static classifiers.

The core component of a rotary air classifier is the rotating element, often a classifier wheel or rotor equipped with blades or vanes (shown schematically in Figure 3b). This rotor plays a crucial role in generating the rotating airflow field, which in turn increases the acceleration of particles. Additionally, the higher rotational speed allows for more precise control over the particle escape window, improving the cut size sharpness of the separation of fine particles. The separation process depends on the balance between the radial drag force and the outward centrifugal force. Fine and light particles, where the drag force is dominant, are carried inwards with the air through the slots of the rotary element and exit towards the fine fraction collection point. In contrast, coarse or heavy particles, where the centrifugal force is stronger, are thrown outwards toward the walls, then fall and exit as the coarse fraction.

The ‘iron triangle’

Rotary classifiers are particularly developed to achieve finer separations compared to the static classifiers, which lack moving parts. For fine powders, such as limestone, quartz and pigments, rotary designs, particularly those with advanced rotor and blade configurations, can achieve cut sizes in the range of 20–100µm [1]. In food processing applications, such as protein-starch separation, the target cut point is often below 10µm, typically lower the size of most starch granules. However, in industrial-scale operations, performance is not solely defined by the sharpness of the cut size.

The overall effectiveness and throughput of the separation process are generally evaluated using three key performance metrics: throughput, yield and purity. Throughput refers to the capacity of the classifier — that is, the mass flowrate of feed material that the classifier can process to make acceptable fractions. The capacity varies greatly depending on the classifier design, size and the material being processed. Sources mention throughputs ranging from approximately 1.5 tons/h for some rotor classifiers handling fine powders, up to 20 tons/h for rotor classifiers used in cement and coal milling [1]. In protein shifting applications, a pilot-scale air classifier has been described with a feed capacity of 200 kg/h [2]. Larger air classifiers designed for coarser solids can handle much higher throughputs, reportedly up to 800 ton/hr [1]. Throughput is a reciprocal index of the energy consumed per unit of processed material, or its desired fraction. It reflects process efficiency, being inversely proportional to the energy consumed per unit of desired material over a fixed operating time. In a typical dry-air fractionation system, the main fan accounts for around 70–75% of total electrical power consumption, with the rotor and feeding system making up the remainder [3].

Yield and purity can be defined according to whether the primary interest is in the fine or the coarse fraction. For instance, when the focus is on fine fractions, yield (recovery percentage), describes what proportion of the desired-size material in the feed is delivered in the fine fraction. For a certain classification system, recovery of specific fractions depends on the material properties and the target cut point, which is pursued by adjusting the operating parameters. Yield takes into account both the purity of the recovered material and the mass ratio of the fractionated desired material relative to its original amount in the feed. Purity is the freedom from oversized material in the delivered fine fraction. This refers to the concentration of a specific component in the separated fine fraction. Purity, often referred to as “sharpness of cut,” means there is little undersize material in the coarse fraction (seen as the hatched blue area in Figure 2) and little oversized in the fines fraction (seen as the hatched green area in Figure 2). Similarly, when the coarse fraction is of interest, yield and purity are defined in the same manner.

In food processing, for example, air classification is used to enrich protein or starch fractions. Protein yield in food applications can vary. Achieving high protein enrichment in the fine fraction might result in yields around 35–44% [4], but this can potentially be increased by recycling the coarse fraction back through the process. The purity of the protein in the fine fractions from pulses typically ranges from 55 to 65% protein [5], although achieving higher purity is challenging and depends on the material and process optimization.

While higher values for these metrics indicate better classification performance, in practice, they are tied to each other in a such a way that improving one performance metric often comes at the expense of another. This inherent trade-off and the strong interdependencies among these three key performance metrics can be explained by the concept of the “iron triangle” of throughput, yield and purity (Figure 4). This conceptual framework, introduced by professor Michael Bradley, has been used to inform insights into industrial challenges brought to The Wolfson Centre for Bulk Solids Handling Technology at the University of Greenwich, U.K. As depicted in Figure 4, for example, measures that enhance both purity and yield usually reduce throughput, limiting the practical applicability at scale. Conversely, operating at maximum throughput often compromises purity, particularly when handling small, light and fragile particles. In contrast, for large, heavy and robust particles, it is generally more feasible to simultaneously achieve high throughput, high yield and high purity, aligning with the ‘ideal’ performance zone shown in the diagram. The practical challenge lies in optimizing the operational parameters to reach an ideal overlap for different materials, especially when working with fine or delicate powders.

Operating practices

Effective air classification relies heavily on choosing appropriate operational parameters. Various operating actions can be taken to optimize the performance of an air classifier for a specific product. Increasing the rotational speed of the classifier wheel or rotor increases the centrifugal force, pushing larger particles outward and resulting in a smaller cut size (that is, finer particles are directed to the fine stream). Decreasing the speed has the opposite effect. Conversely, increasing the airflow rate increases the drag force on particles. This tends to carry larger particles into the fine stream, resulting in a higher cut size. Note that increasing the rotational speed of the classifier rotor results in significantly less excess energy input compared to increasing the airflow rate. Equally important is the feed rate, which influences the concentration of particles in the air stream, and can significantly affect both the separation efficiency and the effective cut size. Moreover, it is directly related to the overall throughput of the system.

Other design features, such as stationary guide vanes or the configuration of the classifier chamber, also influence the airflow pattern and the strength of the forced vortex, thereby affecting the cut size and the flow profile. In some classifier designs, adjusting stationary guide vanes can optimize airflow and particle trajectories, impacting the cut size and allowing the user to adjust the cut size. Furthermore, the physical characteristics of the feed material, such as PSD, density, shape and surface properties (like fat content or moisture content), significantly affect classification performance. Materials with higher fat content can be more challenging to classify due to poor dispersibility.

For many fine classifications, particularly in food and pharmaceuticals, a milling step before air classification is essential. Some advanced milling systems integrate grinding and classification, often using an internal classifier wheel. In these classifier mills, material is ground, and the classifier wheel separates the fines. Oversized particles are rejected by the classifier and returned to the grinding zone for further reduction, creating a closed circuit. Milling reduces the particle size and helps to release or disentangle different components within a material (for example, protein bodies from starch granules in legumes). The type of mill (impact mill, pin mill, jet mill and so on) and the milling parameters (speed, feedrate) are crucial, as they affect the PSD and can cause particle damage (like starch damage), which negatively impacts subsequent classification efficiency.

Air classifiers may use an internal fan to generate airflow. Feed material can be introduced onto a rotating disc and dispersed radially, encountering rotating guide vanes. Coarse particles are influenced by centrifugal forces, settling or moving toward the wall, while fine particles are carried by the airflow. Control over the cut size and flow profile can involve adjusting bottom vanes and the rotational speed of the fan or wheel.

Additionally, pretreatment steps like dehulling or defatting the raw material before milling and classification can enhance separation efficiency, especially in food processing. Optimizing these operating parameters, along with pretreatments, is key to achieving the desired product purity, yield and throughput. Experimental investigations and modeling are necessary to understand the complex interactions and predict classifier performance. Classifier efficiency curves, often determined according to standard procedures like the AIChE equipment testing procedure, are a vital tool for evaluating performance [6].

Overall, current experience shows that with appropriate operating conditions and adjustments, air classifiers can typically achieve all three throughout, yield and purity criteria for large, heavy, robust particles. However, for small, light, fragile particles, only two of the three are realistically achievable in practice.

Applications across industries

Rotary air classifiers are versatile machines employed in numerous industries for particle sizing and separation. In the mineral processing industry, rotary and static air classifiers are used to size minerals for downstream processes like flotation. They are essential for producing fine grades of mineral powders or removing undesirable fines or coarse particles. One example is the classification of molybdenite ore. Classifiers are also used in cement production for classifying ground clinker, as well as coal preparation and the processing of industrial minerals like limestone, quartz and alumina. Capacity can be very high for these applications, due to high density, robust particles and moderately large cut sizes.

Food processing is also a significant area of air classifier application, particularly for dry fractionation of cereals and legumes. The primary goal is often to enrich protein, starch or fiber fractions. Examples include legumes (pea, mungbean, lentil, chickpea, fava bean, cowpea), cereals (barley, wheat, buckwheat, rice), oilseeds (rapeseed, soybean). The protein-enriched fine fraction is valuable for developing high-protein plant-based foods, including meat analogs and beverages. The starch-enriched coarse fraction can also be utilized.

In the production of fine chemicals, catalysts, polymers and other powdered materials, precise particle size control is often necessary for product performance and safety. Rotary classifiers can separate product streams into desired size fractions or remove off-specification material. In the pharmaceutical industry, particle size is critical for drug dissolution rates, bioavailability, powder flowability during manufacturing (tableting, capsule filling) and inhalation properties. Air classifiers are used to produce precisely sized pharmaceutical powders and active pharmaceutical ingredients. In the production of pigments and fillers, rotary classifiers are also used to achieve the desired PSD for optimal performance and quality. The color intensity, opacity and texture of pigments and fillers (used in paints, plastics, paper and so on) are highly dependent on particle size. Also producing abrasive powders with specific particle size ranges for grinding, polishing or cutting applications relies on precise classification.

Challenges and future trends

Despite the wide use of air classifiers and their proven effectiveness, rotary air classifiers face several challenges. Achieving a sharp cut point while simultaneously enhancing both purity and yield is often difficult due to the inherent trade-offs between these performance metrics (Figure 4). Furthermore, the physical properties of the feed material, such as flowability, tendency to agglomerate and stickiness (influenced by factors like moisture, fat and fines content), can significantly affect separation efficiency and system stability. The separation effectiveness is lower when particles have similar physical characteristics (size and density). In food processing, excessive milling can damage particles, creating fragments that may contaminate desired fractions. Fouling, where fine particles stick to classifier components like the wheel vanes, can also negatively impact the overall classification performance.

Energy efficiency is another key concern, especially given the energy demands of upstream processes, such as milling. The integration of the classification step within the broader system design requires careful consideration to minimize energy consumption while maintaining performance. Future developments aim to improve classifier efficiency and selectivity through better understanding of the complex particle-air interactions and flow dynamics. Combining air classification with other dry separation techniques, such as tribo-electrostatic separation, is an active area of research to achieve higher-purity fractions, particularly in food-protein enrichment. Increasing the yield of target fractions, perhaps through strategic recycling of intermediate streams, is also an ongoing focus. This includes developing more sophisticated models and optimizing classifier design and operational control. Although computational tools and theoretical models are advancing, practical insights gained from experimental investigations and expert-led trials remain essential for addressing current challenges and improving classifier performance.

Particle classification plays a vital role in controlling the properties and behavior of solid materials in industrial processes. As the demand for customized particulate materials continues to grow, the importance of efficient air-classification technologies, such as rotary classifiers, remains significant. While air classification is a well-established technique, the drive to achieve finer cut sizes with greater size selectivity introduces new challenges and uncertainties. Therefore, before purchasing or modifying an air-classifier system, particularly for applications beyond its original design, it is essential to thoroughly characterize the material and seek advice from experts in the field. In this regard, small-scale trials remain the most reliable way to gain practical insights. Ongoing collaboration between research institutions and industry will be key to advancing the capabilities of air-classification technologies and meeting future performance demands. 

Edited by Scott Jenkins

References

1. Shapiro, M. and Galperin, V., Air classification of solid particles: a review. Chemical Engineering and Processing: Process Intensification, 44(2), 279–285, 2005.

2. Schutyser, M. A. I. and van der Goot, A. J., The potential of dry fractionation processes for sustainable plant protein production. Trends in Food Science & Technology, 22(4), 154–164, 2011.

3. Insights from technical team in Bradley Pulverizer Ltd. www.bradleypulverizer.com

4. Pulivarthi, M. K., Buenavista, R. M., Bangar, S. P., Li, Y., Pordesimo, L. O., Bean, S. R. and Siliveru, K., Dry fractionation process operations in the production of protein concentrates: A review, Comprehensive Reviews in Food Science and Food Safety, 22(6), 4670–4697, 2023.

5. Skylas, D. J., Whiteway, C., Johnson, J. B., Messina, V., Kalitsis, J., Cheng, S., Langrish, T.A.G. and Quail, K.J., Dry fractionation of Australian mungbean for sustainable production of value-added protein concentrate ingredients. Cereal Chemistry, 101(4), 720–738, 2024.

6. Eswaraiah, C., Soni, R. K., Tripathy, S. K. and Filippov, L.O., Particle Classification Optimization of a Circulating Air Classifier, Mineral Processing and Extractive Metallurgy Review, 40(5), 314–322, 2019.

 

Authors

Hamed Johnny Sarnavi is a research and consultant engineer at The Wolfson Centre for Bulk Solids Handling Technology, University of Greenwich (Central Avenue, Chatham Maritime, Kent ME4 4TB; Email: hamed.johnnysarnavi@greenwich.ac.uk; Website: gre.ac.uk), working on the design of handling and processing units for bulk solids, as well as the characterization, flow and drying behavior of bulk materials. Sarnavi also works on developing proof-of-concept processes for dry air fractionation of plant-based proteins, in collaboration with the Imperial College London’s Bezos Centre for Sustainable Protein. He holds two doctoral degrees, one from Urmia University and the Polytechnnic Univeristy of Milan, and the other from the University of Greenwich.

 

Mike Bradley is professor in particle and bulk technology, as well as the Director of the Wolfson Centre for Bulk Solids Handling Technology. He was awarded both his honors degree and Ph.D. from Thames Polytechnic (now the University of Greenwich) and, as manager and director, provides technical leadership in all aspects of bulk solids handling. His particular areas of interest lie in pneumatic conveying, design of hoppers and silos, dust control, plant integration and maintenance of product quality. Bradley is chair of Solids Handling and Processing Association (SHAPA), and is a member of the Materials Handling Engineers Association (MHEA) and of the Institution of Mechanical Engineers Bulk Materials Handling Committee (IMechE). He was awarded a professorship in 2006 and the directorship in 2008.