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Advanced Size Measurements and Aerodynamic Classification of Solid Recovered Fuel Particles Gregory Dunnu, Thomas Hilber, and Uwe Schnell* Institute of Process Engineering and Power Plant Technology (IVD), UniVersity of Stuttgart, Pfaffenwaldring 23, 70569 Stuttgart, Germany. ReceiVed February 1, 2006. ReVised Manuscript ReceiVed May 23, 2006
Solid recovered fuels are highly heterogeneous mixtures generated from high calorific fractions of nonhazardous waste materials intended to be fired in existing coal power plants and industrial furnaces (CEN/ TC 343 N 9rev3, Solid Recovered Fuel, 2003). These materials are commonly irregular in shape and size. It is therefore not easy to predict their aerodynamic properties, especially in comparison to pulverized fuels. Because particles fall with their maximum projection area perpendicular to the direction of the fall, a size measure representing this maximum projection area is most likely to relate to behavioral properties (Sneed, E D.; Folk, R. L. J. Geol. 1958, 66, 114-150). In view of this, the ability to describe precisely the largest projected area of a particle will immensely help in any modeling of particles’ behavior in boilers and industrial furnaces. The particle image analysis method (PIAM) is an automatic image processing tool developed with MATLAB-coded commands that is capable of determining several properties of irregular-shaped particles including the largest projected area, equivalent diameter of a sphere of the same projected area and mass, major and minor axis lengths of each particle, eccentricity, and many more. PIAM processes digital images of particles to determine their properties in terms of picture elements (pixels). With the magnification factor, pixel distances can be converted into real distances. The particle information obtained from PIAM is used to characterize behavioral properties.
Introduction The extraction of the physical properties of irregular-shaped particles has been attempted for a long time, and efforts are still underway to find the best means to describe them comprehensively. Although some researchers have questioned the amount of information contained in the size and our ability to extract such information,3 the attempt continues. Part of the problem in the analysis of particle size is that it cannot be determined independently of the particle shape. As a result, many techniques for particle-size determination have been developed and used, each measuring a different aspect of particle size or particle behavior that is related to size. The rapid increase in computer and related technologies has spawned new size analyzers. The absorption of X-rays, light transmission, electrical conductivity, and laser diffraction are examples of new techniques.4 An additional class of sizing techniques is based on the analysis of data obtained from an image and is referred to herein as image analysis size (IAS). Various instruments use different images and define size in different ways. In fact, an IAS may allow for more than one aspect of size to be analyzed: the maximum projected area of the particle, major and minor axis lengths, equivalent diameter, centroid, eccentricity, and many * Author to whom correspondence should be addressed. Tel.: +49-711685-3574. Fax: +49-711-685-3491. E-mail:
[email protected]. (1) CEN/TC 343 N 9rev3, Solid Recovered Fuel, 2003. (2) Sneed, E D.; Folk, R. L. Pebbles in the Lower Colorado River, Texas, a Study in Particle Morphogenesis. J. Geol. 1958, 66, 114-150. (3) Ehrlich, R. Size Analysis Wears No Cloths, or Have Moments Come and Gone? J. Sediment. Petrol. 1983, 53, 1. (4) Syvitski, J. P. M. Principles, Methods, and Application of Particle Analysis; Publisher: Location, 1991; ISBN 0-521-36472-8.
more.5 A new approach to IAS, the particle image analysis method (PIAM), involves the use of software for particle analysis. It is a tool developed for the purpose of extracting information from solid recovered fuel (SRF) particles to enable their shape and size characterization. There are numerous definitions for the term SRF, but the recent one put forth by CEN/TC 3431 defined it as “fuel prepared from non-hazardous waste to be used for energy recovery in waste incineration and co-incineration plants”. Figure 1a,b below shows a type of SRF called Sekundaerbrennstoff (SBS) and its percentage composition. SRF is typically a heterogeneous mixture, and it is intended to be fired in existing coal power plants and industrial furnaces. These materials are commonly irregular in shape and size. It is therefore difficult to apply conventional particle-size analysis methods, for example, sieving, to analyze them. Particle Image Analysis Method (PIAM) PIAM is a program written in MATLAB code that has the capability to read a digital image of particles and further measure a set of properties for each labeled particle region. It is fully automatic once the name of the image file is entered into the command prompt. During the edge acquisition process, PIAM utilizes the contrast between the particles and background to outline them. The outline of each particle is then filled, and the characteristic properties of the filled areas are computed in picture elements (pixels). PIAM can process more than a hundred particles in an image at a time; however, these two conditions should be fulfilled in order (5) Kennedy, S. K.; Mazzullo, J. Image analysis method of grain size measurement. Principles, Methods, and Application of Particle Size Analysis; Publisher: Location, 1991; ISBN 0-521-36472-8, Vol. 6, pp 76-87.
10.1021/ef0600457 CCC: $33.50 © 2006 American Chemical Society Published on Web 06/29/2006
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Figure 2. Target image of particles with a reference shape at the topleft corner.
Figure 1. (a) Solid recovered fuel. (b) Composition of SBS.
to achieve desirable results during edge acquisition: (1) particles should be discrete and should not touch one another, and (2) there should be sufficient light intensity contrast between a particle and its background. The particle edges are defined by a fixed threshold value of light intensity separating particles from the background. The following terminologies are characteristic parameters computed by PIAM:6 FilledArea. The number of pixels that correspond to a projected area region of a particle, with all of the holes filled in. EquiVDiameter. The diameter of a circle with the same area as the projected area region of a particle, computed as EquivDiameter Deq )
Area x4 × Projected π
(1)
MajorAxisLength. The length (in pixels) of the major axis of the ellipse that has the same second moments as the region of the projected area of a particle. MinorAxisLength. The length (in pixels) of the minor axis of the ellipse that has the same second moments as the region of the projected area of a particle. Eccentricity. The eccentricity of the ellipse that has the same second moments as the region of the projected area of a particle. The eccentricity is the ratio of the distance between the foci of the ellipse and its major axis length. The value is between 0 and 1 (0 and 1 are degenerate cases; an ellipse whose eccentricity is 0 is actually a circle, while an ellipse whose eccentricity is 1 is a line segment). All of these properties are scalar quantities and are supported only for 2-D input. The scale factor for the conversion from the pixel distances to real distances can be calculated using the magnification and distance from the camera lens to the object. Other measures can also be (6) MATLAB help, release 13, image processing toolbox.
Figure 3. Outlines of particles under investigation.
adapted to carry out this conversion, which include using a referenced shape with a known area in each image to be processed. PIAM can only read images in “*.tif” format, so the first step is to save the target image in this file format in the current directory of the MATLAB window. The name of the file is entered in a command prompt of PIAM, and it takes less than a minute to obtain results. The time it takes to process an image increases with the number of particles in it. Figure 2 represents the image of the particles whose properties are to be determined. The circular object on the top-left corner is a reference shape, and its properties are already known. In this example, a reference shape is used because it is simple and straightforward rather than using the magnification factor. Figure 3 shows the first stage of the property acquisition process. PIAM uses the intensity of light difference between the particle and its background to outline it. In Figure 4, the particle outlines are filled. The discrete white portions are illuminated pixels (onpixels), and they represent individual particles, whereas the dark background represents unilluminated pixels. The type of particles that can be analyzed by PIAM varies widely. The important issues are that, if a particle can be seen in the image and fulfils the two conditions stated earlier, PIAM can process it. It should be noted that the accuracy of the results depends strongly on the quality of the input image. Distortion of the particle image normally occurs as a result of polarization by the optical lens when the object is laterally displaced from the focal center of the camera lens. The further the lateral displacement is, the greater the distortion of the particle image. For instance, a circle when distorted in an image looks like an ellipse, and PIAM would likewise treat it as such. However, an approach to minimize the polarization is to concentrate a few particles within the center of
Solid RecoVered Fuel Particles
Figure 4. On-pixels within the particle outline.
Figure 5. (a) Outline of the reference object. (b) Reference object filled with on-pixels.
the camera focus or to use a high-resolution scanner to acquire the particle image. Precautions should however be taken when using a scanner; for example, a transparent foil can be put on the scanner surface before the particles are placed on them to avoid surface scratches on the scanner. In this case, the distortion of the image is very minimal. To test the accuracy of PIAM, a digital camera was used to snap several reference circular objects of known diameters (Figure 5a,b), making sure that the objects lie within the center of the camera focus. The reference images were analyzed with PIAM, and the results when compared with the original values showed errors of about ( 1% to ( 2% at a 98% confidence level, indicating that only minor errors are involved in measuring particle-size properties with PIAM.
Settling Velocity Model for Solid Recovered Fuel Particles Aerodynamic Surface Area of SRF Particles. One of the fundamental problems in studies on the role of particle shape in settling behavior is that it has not been possible to define the shape with a single quantitative expression. An attempt to do so would be to determine the property that has the most influence on the particle settling behavior. This will be based on the fact that particles fall with their maximum projection areas perpendicular to the direction of the fall;7 therefore, a size measure representing this maximum projection area is most likely to relate to behavioral properties. This argument was also used by Sneed and Folk2 in their definition of maximum projection sphericity, which will not be discussed in this paper. The shape properties of irregular particles are often expressed by parameters based on the longest, shortest, and intermediate orthogonal axes or by a triaxial measurement.8 Flemming9 suggested (7) Krumbein, W. C. Settling velocities and flume behaviour of nonspherical particles. Trans. Am. Geophys. Union 1942, 41, 621-33. (8) Heywood, H. Numerical definitions of particle size and shape. Chem. Ind. (London) 1937, 56, 149-154.
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Figure 6. Translation of an irregular object (SRF particle) to an equivalent sphere of the same projected area and mass as that used by Dunnu.10
13 dimensional parameters as being necessary to describe a particle. All of these are strenuous in the aerodynamic classification process of SRF materials, and they do not wholly represent the aerodynamic surface of a particle. Dunnu10 used two-dimensional parameters, the greatest length (L) and the greatest width (B), to describe the aerodynamic surface area of SRF particles. The assumption that SRF particles will reach a terminal settling velocity that can be assigned to a sphere having the same projected area and mass was stated. The volume of the sphere can therefore be used as an approximation to that of the SRF particle in a situation where the terminal settling velocity is to be estimated. Figure 6 illustrates this assumption. All of the above-stated approaches have success to some degree, but an effective way to describe the aerodynamic surface area of a particle would be to measure the whole maximum projected area of a particle. PIAM does exactly that and more. Settling Patterns of Nonspherical Particles. The fluid forces acting on a settling particle can be resolved into a pressure force exerted normal to the surface of the particle and a viscous shear stress acting tangential to it. In the case of spheres settling at Reynolds numbers (Re) less than about 0.5 Re )
FWDeq µ
(2)
where F is the fluid density, Deq is the equivalent diameter of the particle, W is the settling velocity, and µ is the dynamic viscosity, one-third of the total drag force is due to pressure drag and twothirds to viscous drag.11 At higher Reynolds numbers (more than 24 for spheres), flow separation comes into play and pressure forces become the dominant influence. The formation of a turbulent wake with negative pressure (relative to the ambient pressure) at the back of the particle adds to the total drag force, which therefore depends partially on the proportion of the particle surface covered by the wake. As the line of flow separation shifts forward with higher Reynolds numbers, the pressure drag force increases proportionally. Considering these factors, it is to be expected that the surface area of the particle normal to its direction of settling would play an increasingly larger role at higher Reynolds numbers.12 A study carried out by Komar and Reimers13 revealed that ellipsoidal particles generally settle with their maximum projection (9) Flemming, N. C. Form and function of sedimentary particles. J. Sediment. Petrol. 1965, 35, 381-390. (10) Dunnu, G. Design, Setup and Testing of a Lab-Scale Setup to characterize the aerodynamic properties of solid recovered fuels. Unpublished MSc. thesis no. 2759, IVD Universita¨t Stuttgart, Stuttgart, Germany, 2005. (11) Stokes, G. G. On the effect of internal friction on the motion of pendulums. Cambridge Philos. Soc., Trans. 1851, 9 (2), 9-106. (12) Le Roux, J. P. A hydrodynamic classification of grain shapes. J. Sediment. Res. 2004, 74, 135-143. (13) Komar, P. D.; Reimers, C. E. Grain shape effects on settling rates. J. Geol. 1978, 86, 193-209.
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Figure 7. SRF particle size distribution based on the equivalent diameter.
area normal to the fall direction. This is due to a pressure gradient developing on the fronts of particles oriented oblique to the direction of settling, which varies from a maximum at the stagnation point near the leading edge to a minimum near the trailing edge. The combined effect tends to swing the particle around the center of mass to adopt an orientation normal to the flow.14 Willmarth et al.15 and Stringham et al.16 also observed that, at relatively low Reynolds numbers, disklike particles settle in a steady-flat pattern oriented normal to the flow, which they attributed to viscous forces damping out the oscillations produced by the pressure moment. The same behavior was observed for prolate and oblate spheroids as well as cylinders.17 Again, at higher Reynolds numbers, disklike particles may assume a varying sequence of orientations, which is related to the moment of inertia. Such particles may therefore settle in a sideto-side oscillating pattern, a glide-tumbling or tumbling pattern, so that the drag coefficient continually changes. Other shapes also undergo oscillation or rotation about their vertical axis at Reynolds numbers exceeding certain values.16 These settling patterns are not considered here because of the complications arising from such behavior. In developing a settling velocity model for SRF particles, the equivalent diameters (Deq) of the particles were determined using PIAM. Their corresponding equivalent volumes (Veq), defined herein as the volume of the sphere with the same projection area as the particle, were calculated using eq 3. With the individual particle masses (m), the apparent densities (FA) of the particles were calculated with eq 4. π Veq ) Deq3 6
m Veq
Results and Discussion
(4)
eqs 2, 5, and 6. CD )
24 4 + + 0.4; (transient area, 0.5 < Re < 103) (5) Re xRe
where Ff is the fluid density and CD is the drag coefficient. WS )
x
4(FA - Ff) 1 gDeq ; (0.5 < Re < 103) 3Ff CD(Re)
Sieve analysis, which is the most commonly used method for particle-size analysis, is not suitable for the detailed analysis of SRF particles, the reasons being the heterogeneous nature of SRF, varying particle densities, and fluffy materials entangle each other and agglomerate during sieving. However, sieving can be used to prepare the sample by way of separating smaller fractions from larger ones. The two fractions can then be analyzed with PIAM separately. There are no upper and lower limits for PIAM in terms of particle-size measurements. In the case of minute particles, a digital camera coupled with a microscope can be used to capture an enlarged image. There are other image-analytic techniques (e.g., Videoplan18 and Arthur system5), all of which have limitations because they are particle-by-particle techniques, therefore making them inconvenient to analyze large numbers of particles per sample (e.g., 1500 particles). The application of PIAM is widespread even in the field of sedimentary petrology, where the eccentricity of the sediment is used to predict its transportability. As mentioned earlier, the various sizing techniques analyze different aspects of particle properties, and the technique one chooses depends on the objectives of the analysis: In PIAM, we have the privilege of simultaneously analyzing several characteristic properties such as the maximum projection area, equivalent diameter, major axis length, minor axis length, and eccentricity for more than 50 particles in an image during a single run.
(3)
Finally, the settling velocity (WS) is calculated by iteration using FA )
Comparison of Particle-Size Distribution Techniques
(6)
An analysis of a representative sample of SRF was conducted to determine the size distribution using the sieve analysis method and PIAM. Figure 7 shows the results acquired from the two methods. (14) Middleton, G. V.; Southard, J. B. Mechanics of Sediment Movement, lecture notes for short course No. 3; Eastern Section of the Society of Economic Paleontologists and Mineralogists, 1984. (15) Willmarth, W. W.; Hawk, N. E.; Harvey, R. L. Steady and unsteady motions and wakes of freely falling disks. Phys. Fluids 1964, 7, 197-208. (16) Stringham, G. E.; Simons, D. B.; Guy, H. P. The behavior of large particles falling in quiescent liquids. U. S. Geol. SurV. Prof. Pap. 1969, 562-589. (17) Komar, P. D. Settling velocity of circular cylinders at low Reynolds numbers. J. Geol. 1980, 88, 327-336. (18) Scha¨fer, A. The Krontron Videoplan, a new device for determination of grain size distributions from thin sections. Neues Jahrb. Geol. Pala¨ontol. Monatsh. 1982, 2, 115-128.
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Figure 8. Effect of a large projection area of SRF on settling velocities.
The particle-size distribution curves from the two methods interlock at a point very close to the d50 mark (9 mm). The curves then depart from each other at the lower and upper percentiles of the distribution. The departing behavior of the curves signifies the different properties measured by sieve analysis and PIAM, without regard to the problem of agglomeration during the sieving process. The problem of agglomeration during sieving of the SRF materials has been experienced because of the components of SRF, as explained earlier. Another setback of the sieving results when it comes to simulation of the combustion system on the basis of computational fluid dynamics calculations or artificial neural networks is the drag forces affecting the particle aerodynamic behavior. The sieve analysis method thus provides data that include little or no aerodynamic properties of SRF particles. It therefore gives misleading results as to the residence time of the fuel in boilers. On the other hand, the size-distribution curve derived from PIAM measures the particle size relative to its maximum projection area, which has the most influence on the particle aerodynamic properties. The largest projection area of SRF crucially affects its aerodynamics, and knowing the exact value will greatly improve the determination of drag forces acting on the SRF particles in a boiler system. The particle size data obtained from the two size analysis methods were used to determine the settling velocities of the SRF particles on the basis of Reynolds number calculations (eqs 2-6). These velocities were plotted against experimentally acquired settling velocities of single SRF particles. Figure 8 compares the relationship between the various velocities with an ideal situation. Line A is an ideal line showing equality in data in both experimental and Reynolds-based calculations. It passes through the origin with a gradient of 1. An experimental settling velocity distribution of the SRF particles was generated by settling techniques (a drop-tube experiment); their corresponding theoretical settling velocities were calculated using particle size information obtained from PIAM and sieve analysis. The relationships between the theoretical velocities and their corresponding experimental values were compared to the ideal situation. Line B, which represents PIAM, is closer to the ideal (line A) relative to the sieve analysis (line C). The relatively lower settling velocities shown by line B can be attributed to
the increased drag forces acting on the particles. According to Rubey,19 at high Reynolds numbers, the drag force is proportional to the square of the particle radius, and because the size distribution from PIAM shows larger particle sizes, they will be greatly affected by drag forces and therefore lead to lower settling velocities. Sieve analysis, unlike PIAM, can wrongly classify particles simply by the orientation of the particles of which they bounce on the sieve. Particles mostly slip through sieves when their shortest sides are correctly aligned to the sieve aperture; this does not happen in PIAM. The higher settling velocities depicted by line C are a direct effect of lesser drag forces acting on particles because of the relatively smaller diameters reported by the sieve analysis method. In an attempt to simulate the aerodynamic behaviors of SRF, for example, particle trajectories, settling velocities, flame position, and so forth, in any combustion system, a precise relation for the calculation of their drag forces is essential; therefore, a precise definition of the particle’s largest projection area is an important initial step. Conclusions PIAM does not require sophisticated hardware, as is the case in many image analysis size systems. It can run on any standard personal computer with MATLAB installed. The speed of execution is proportional to the computer processor performance and the number of particles in the image. The setup for PIAM is very simple and less-expensive for high-precision particle size measurements. It is a quick and efficient way to determine a set of characteristic parameters of irregular-shaped particles. It requires a minimal amount of data management, and it is very capable of handling widespread size distributions. With the exception of transparent particles, PIAM has no limitations with respect to particle color, shape, or amount. It is an effective particle size analyzer. Sample preparation for digital image capture is quite laborious and therefore makes the system unsuitable to process a large quantity of particles. Therefore, the sample size should be less than 1500 particles. (19) Rubey, W. W. Settling velocities of gravel, sand, and silt particles. American J. Sci. 1933, 25, 325-38.
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The determination of the aerodynamic properties of any material is far from straightforward, and it is not easy to develop a single formula or approach to determine such properties. It can be noted that the settling velocities of particles are affected by their aerodynamic nature: In practice, if a property of interest depends strongly on a character of a particle that can be measured or calculated, one can simply determine this character and relate it to the desired property of interest. This is an indirect approach, and it has been applied herein to determine the aerodynamic properties of SRF particles. The results explained that PIAM better defines the projected area of a particle than the sieve analysis method and, for that matter, improves the relationship between the settling velocity and equivalent diam-
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eter on the basis of the largest projection area of a particle, and they also explained that the influence of drag forces on particle settling velocities is very significant, and its effect should be wholly captured. This research reveals the importance of using particle size data that include aerodynamic properties in any combustion systems that involve the use of solid recovered fuels. Abbreviations B, greatest width; IAS, image analysis size; L, greatest length; PIAM, particle image analysis method; SBS, Sekundaerbrennstoff; SRF, solid recovered fuel. EF0600457
