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Sieve size analysis is a well-known and important tool for the characterization of powders over a wide range of materials and applications. Everything from ground coffee to abrasive material is priced and sold based on measured sieve fractions, and many reference methods require the use of sieves.
Though sieve size analysis has been around for thousands of years, it is still in wide use for a number of reasons. First, it is a simple and inexpensive tool. No complicated sample preparations are needed beyond simply pouring the powder into the top sieve. Secondly, sieves can test large amounts of powder-literally hundreds of grams. In contrast, many of the more modern and high-tech particle sizing methods, like laser light scattering, might ascertain particle size from just micrograms or milligrams of material. The statistical accuracy of sieve data will therefore be much better. Finally, sieves directly provide mass-weighted particle size distributions, something that no other technique can do.
Unfortunately, the use of sieves is very labor-intensive and involves long measurement times (after the powder is added, vibratory energy must be applied and each sieve has to be reweighed, cleaned and dried). In addition, sieving can be very problematic for fragile powders because the vibratory energy required to separate the particles can also fracture them and cause the size distribution to change. Another disadvantage with sieve size analysis is that the sieving time is one of several factors that can significantly affect the final results and must be controlled. As a result, the accuracy of sieving results can be dependent on the skills of the operator. Furthermore, the long measurement times preclude the use of sieve size analysis as a process monitoring tool.
With the development of digital image capture technology and inexpensive and fast desktop computing, a new instrumental solution now exists that enables statistically accurate and fast sieve size measurements. Process engineers can monitor powder particle size on- or at-line and obtain much-improved control over the specifications of their final powder product.
Developing an Automated SolutionWhen properly implemented, the use of automated image analysis (IA) can provide actual sieve size results on a large aliquot of powder without any prior correlation with actual sieve data. Three issues had to be addressed when determining how to implement this solution. The first challenge was introducing the powder particles to the camera in a way that was fast and produced images of the particles in controlled orientations.
It is important to know that automated image analysis tools have two common configurations.1 The first configuration, dynamic image analysis (DIA), uses a conveyor belt or vibratory feeder to drop powder in front of the camera. Because the particles are under the control of gravity, only a moderate amount of material can be imaged and the orientation of the particles cannot be controlled as they fall. Thus, it is possible for flake-like particles that might normally be captured in a smaller (larger-sized) mesh sieve to be imaged from a dimension that would suggest a smaller size. The second configuration is that of static image analysis (SIA), where particles are introduced to the camera after dispersal on an optical surface. While this method provides exact orientation control, only a minute amount of material is imaged.
The new solution combines these two configurations to capitalize on their individual advantages. The powder is introduced to the camera by a laminar horizontal air flow that passes between two closely spaced glass windows (see Figure 1). The air flow is produced by a simple vacuum pump. Due to the laminar flow conditions and significant acceleration, particles are oriented with their largest surface facing the camera at the instant of image capture. The use of a telecentric lens produces a large depth of field that aids in keeping all of the particles in focus, thereby maximizing detail. The particle containment design ensures that the electronics, optics and other components are not contaminated, and allows the operator to run hundreds of analyses without any cleaning. In addition, the particles achieve such a speed that up to 500 g of material can be imaged per minute.
The second issue was to define a size parameter that would produce results that match with sieve size analysis. It may come as a surprise to those unfamiliar with particle size analysis that particle size or particle diameter is a not a well-defined parameter (except for spherical particles); in reality, many definitions exist.2 Most particle size analyzers do not measure particle size directly. Instead, they measure a physical property of the particle that is related to size, like sedimentation in a liquid, the scattering of light, and so on. Particle size is then defined relative to the measured physical property, so a particle might sediment like a 60-micron sphere, scatter light like an 80-micron sphere, or fit through a 300-mesh sieve.
If we think about how sieves actually select for size, we can conceive of a particle diameter that is equal to the size of the largest sphere that fits within the measured perimeter of the particle (see C in Figure 2). We can call this the sieve diameter. It is easy to see that this size, directly measured from the image data, can provide a well-defined particle size that will produce data that should match sieve size analysis.
The final piece of the puzzle was to properly estimate the particle volume in order to infer a mass distribution. In sieve size analysis, this is done directly by weighing the amount of material in each sieve fraction. It presents a special problem for image analysis, however, as only two dimensions of each particle are measured. From stereological theory, we know that the volume of an object is proportional to its projected area. Though it cannot help us measure the absolute particle volume, we can use this information to determine the relative volume of the particles, which in turn allows us to produce a mass distribution (assuming uniform density). The volume estimator is based on the measured area of the particle and a specially defined mean diameter.
Achieving Accurate DataHow does this combination of fast and oriented particle introduction to the digital camera, carefully selected definition of particle size, and volume fraction estimator work in terms of producing accurate sieve size data? A working version of the device is shown in the lead image. Powder is loaded into the vibratory feeder on the left. The powder drops into the air stream, which moves through the rectangular-shaped pipe. The light source (monochromatic collimated blue light for best resolution) is mounted underneath the pipe and protected by a removable glass window. The digital camera and telecentric lens, positioned above the light source, are also protected by a removable glass window.
To determine whether the method would provide accurate and precise sieve data, a 100-g aliquot of BCR 68 (a quartz material distributed by the Community Bureau of Reference for use as a sieve size analysis standard) was tested. The mass distribution was certified by sieve size analysis at five separate laboratories and can be seen in Table 1.
For more information, contact Particle Sizing Systems, 8203 Krystel Circle, Port Richey, FL 34668; (727) 846-0866; fax (727) 846-0865; e-mail email@example.com; or visit www.pssnicomp.com.