Measuring Segregation of Steak Seasoning Using a Segregation Tester

Segregation data finds two uses. Engineers might intend to use the segregation data to improve product design to manufacture a product with minimal segregation. They might also intend to alter the processing to reduce the impact of segregation in their handling facility or plant packaging process.

In both cases, the segregation mechanism, segregation pattern, and segregation magnitude are all important parameters in product design or process recommendations to consider.

Bulk Segregation: Causes and Mitigation

A range of mechanisms [1, 2] can cause bulk segregation. Finer particles that sift down through a matrix of coarse particles [3] tend to separate as they slide down a pile. Air currents produced by free fall convey the finer particles to regions in the bin where gas velocities are low enough to precipitate the particles [4].

Variations in repose angles could make materials slide down the pile at different velocities, thereby leading to a separation of materials based on particle friction characteristics [5–7]. To prevent the segregation problems associated with bulk materials, it is essential to gain insights into the mechanisms that cause segregation and even the segregation magnitude.

It is also important to understand the segregation pattern in process vessels. A segregation problem brought about by the air entrainment of finer particles can be solved only by modifying the amount of air entrained in the free-falling solid. However, segregation caused by sifting cannot be solved by modifying the free-fall height.

One issue with existing segregation testers is that they are not very well correlated to the process. Two or three materials could segregate due to a difference in properties and if the mixture experiences sufficient external stimuli.

The addition of sufficient fluidization gas will lead to the separation of materials with different densities, although such extreme conditions may not occur in the real process. The actual question to be solved is: Will the mixture segregate upon being subjected to a feed behavior analogous to the one that exists in the process? Hence, any segregation tendency measurement must involve three important elements.

Firstly, it is necessary for the feed to be controlled to enable the measured segregation to be scalable to process conditions. Secondly, the segregation pattern must be included as part of the mechanism to be able to predict the anticipated concentration that leaves the process equipment. Lastly, it is essential to quantify the segregation magnitude to offer guidance and to determine whether the mechanism is really a potential problem in the process.

Segregation Measurement Device and Procedure

The perfect way to perform a segregation test would be to, firstly, feed the material into a small container in a way analogous to the original process conditions. Subsequently, the pile must be sectioned at many (about 15 to 50) different points, and finally, the concentration of important components in a small amount of material collected at these points must be measured.

This process would produce sufficient information related to the segregation mechanism, magnitude, and pattern of multi-component materials that will be useful in product and process design.

Previously, scientists have fed bulk materials onto a pile, divided a conical pile into annular sections, and carried out size or chemical analysis on the material collected in every annular section [8, 9]. This is a long and time-consuming technique which leads to very few measurements being collected along the pile. Most often, plant engineers prefer thieves to sample the piles in process equipment.

Even this technique yields only a few measurements along the pile and agitates the surface, therefore modifying the segregation pattern. Others involve measuring the pattern of segregation by positioning the material in a funnel flow hopper and subsequently discharging the hopper, gathering the output stream, and measuring the mixture quality by particle size or chemical composition [10, 11].

This leads to convolution of the segregation with the flow pattern from a specific bin or hopper. If the bin design in the tester is not exactly the same as that in the process, then it cannot be assured that the composition discharged from the tester will be analogous to that found in the real process. Moreover, flow from the segregation tester hopper could be hindered by the cohesive properties of the bulk material, although hang-ups might not occur in the process with the same material.

Researchers argue that it is beneficial to measure the pattern of segregation from the pile formation segregation tester and to subsequently calculate the velocity pattern from flow properties. The composition of material that leaves the hopper can be evaluated by knowing the velocity profile and the segregation pattern.

This technique offers the additional advantage that upon measuring the segregation pattern, the concentration yielded from any series of general hoppers, bins, and other process equipment can be evaluated to know the effect of segregation on the overall process. As a result, this technique offers a robust tool for predicting and mitigating segregation issues in feed systems and not only within a single hopper or bin.

More than two components are included in most of the real process mixtures. But mostly, segregation studies are performed only for two-component combinations [12–19] since analyzing for multiple materials is not only challenging but can also be tedious. In general, the interaction of two materials is simple and follows predictable rules.

One such simple rule is that the voids are filled by the fine particles. In a two-component system, this makes the fines accumulate at the top of the pile. The application of simple rules in a two-component system often leads to predictable behavior.

The real difficulty is to estimate what takes place in systems including multiple components that undergo one or more segregation mechanisms. Some engineers have created models that include multi-particle effects [20–22]. Simple rules may not be applicable to multi-particle flows. The pattern of segregation of a mixture that contains over two components is considerably more complex owing to the interaction of several sets of particles.

At times, this can result in predictable patterns. For instance, materials with varying repose angle will usually concentrate along a pile surface in order of their repose angle, where the flattest repose angle is at the bottom of the pile and the steepest is at the top.

In case a material undergoes both sifting segregation and angle of repose segregation, increasing the local fine particle concentration will lead to an increase in the local repose angle on the pile, thereby making a material that did not segregate previously to separate from the bulk due to local repose angle variations.

Similarly, when the voids between particles are partially filled with the correct size particle, a smoother surface can be created, which will enable segregation of other materials in the mixture when they slide down a pile.

When one component is changed, it usually has an impact on the segregation profile of all the materials in the mixture. In any case, the potential to measure the pattern of segregation of a mixture that includes multiple components is vital to gaining insights into the segregation behavior of the mixture.

One method for gaining access to the cross-section of the pile is to fill a slice model with material and use optical techniques to notice the segregation pattern from the side of the slice model (see Figure 1).

Schematic of segregation tester.

Figure 1. Schematic of segregation tester.

Dump material into box and observe the change in color intensity along the pile as measured just below the top surface of the pile (rectangle section).

These changes in color intensity are an indication of differences in either chemical composition or in particle size and can be used to estimate the segregation of key components in the system.

Observing the segregation along the sides of a slice model will be skewed to the optical pattern occurring at the wall of the segregation tester. Therefore, it is essential to exercise care while loading the tester to ensure that a representative sample is visible along the side of the tester.

When the tester is filled across the width of the slice model, the material is distributed to the tester wall which forms a uniform material over the tester. If the thickness of the slice model is limited, it will be helpful in distributing the representative material to the tester wall.

Very thin slice models experience banding caused by wall effects that would not exist in wider slice models. In the case of typical materials, the thickness of the slice model is limited to a minimum of roughly 25 mm.

Measurement zone along the pile top surface.

Figure 2. Measurement zone along the pile top surface.

Define the size of the viewport and measure the spectra along the top of the pile. Adjacent viewports can overlap and the tester can measure concentrations at up to 50 locations along the pile.

Subtle differences in color can be measured by using reflectance spectroscopic methods. Multiple measurements are needed to identify the average concentration within a given view-port while dealing with a discrete particle system.

Segregation measurement is also a scale issue. It is essential for the size of the chosen viewport to be large enough in order to hold a representative number of particles, yet sufficiently small so that variations in local compositions are not lost in the averaging scheme. The perfect segregation tester will enable the user to vary the size of the viewport, and also the number of measurements within the viewport to achieve segregation measurement.

Using the principles discussed above, an innovative segregation tester (shown in Figure 3) was developed to measure the segregation pattern of different materials. The tester includes a vibratory feed system for controlling the fall height and rate of material fed into the tester slice model. The slice model’s back part is made of glass to enable the reflectance probe to view the side of the pile, where the probe is connected to an x-z stage that travels to observe any part of the slice model hopper.

Segregation tester used in the experiment showing the variable speed vibratory feed system and the pile formed in the tester for analysis (note that the right door is open to expose the segregation hopper).

Figure 3. Segregation tester used in the experiment showing the variable speed vibratory feed system and the pile formed in the tester for analysis (note that the right door is open to expose the segregation hopper).

When the light from this probe is sent to a spectrophotometer, spectra of the material are produced in the viewport area. In addition, there are nearly six component trays containing the pure component materials that can be viewed with the spectrophotometer. During filling, the test unit’s front part is opened and it is closed at the time of spectral measurement. The black glass doors are designed such that they prevent ambient light from having an effect on the spectral imaging results.

The use of the tester as a medium that provides product design recommendations was demonstrated by selecting steak seasoning as the material of choice since it comprises of five components of different shapes, sizes, and colors.

Assuming that there is liberty to choose one of the three grades of salt as one of the components in the seasoning mixture, the question to be solved is as follows: which salt grade offers the most uniform mixture and is the least sensitive to segregation problems?

In this experiment, steak seasoning comprising of 47% salt, 19% black pepper, 28% minced garlic, 4% dill seed, and 2% red pepper was used. The procedure described below was used for generating the steak seasoning segregation data:

  1. One of the three possible grades of salt is used to create a mixture of steak seasoning.
  2. The mixture is fed into the segregation tester slice model through a drop height of 152 mm at a rate of 1.5 liters per minute.
  3. Salt, black pepper, garlic, dill seed, and red pepper are placed in the individual component measurement trays and the reflective spectral response of each material is measured for 1,825 wavelengths between 420 and 850 nm.
  4. A measurement zone parallel to the pile surface is chosen and positioned 20 mm below the top surface of the pile.
  5. A 15 mm x 15 mm square is selected as the viewport and the spectra of 36 (a 6 x 6 matrix) distinct points within this viewport are measured. These 36 values are averaged to create an average spectral reading of the material inside the selected viewport.
  6. Along the pile 20 points are selected to measure the average view-port spectra (a total of 720 complete spectral measurements per pile).
  7. The spectra of the pure components and the average mixture spectra along the pile are used to compute the concentration of the pure components using numerical spectral de-mixing methods. This yields the concentration of the different materials along the pile as a function of radial distance to the edge of the pile.
  8. Steps 1 to 7 are repeated for each salt grade.

The averaged spectra of the five components used in the mixture are shown in Figure 4. The different peaks at different wavelengths can be observed in the spectra. The spectra of salt and garlic are similar. While the spectra of black pepper and dill seed are similar, red pepper has a unique spectrum.

Average spectra such as these were developed by filling pure materials into the component holders and averaging 10 individual spectral images of each pure component. The effect of particle size, color, and surface texture is included in these pure component spectra. Particle orientation also has an effect on the spectra.

Spectra of steak seasoning components.

Figure 4. Spectra of steak seasoning components.

In case all the particles were of the same size and all spherical, then the optical difference between the particles in the mixture and pure particles filled in the component trays would be zero. Here, the spectral intensity of the mixture, Fmixj(λ), would be a simple linear combination of the pure component’s spectral intensity (Fi(λ)) based on the local fraction (xi,j) of each component (see Equation 1).

(1)

Yet, here, the voids between coarse particles are filled by the smaller particles, forming a shadow effect for the coarse particles. A proportionally higher percentage of area is occupied by the fine particles within the voids compared to that suggested by the volume fraction. This specifies that the mixture spectra are not accounted for by a linear combination of pure spectra.

The mixture spectra will be more biased toward the fine materials. A weighting factor (Wi) can be added to the linear combination of pure spectra to model this effect (see Equation 2).

(2)

A list of the calculated optical weighting factors for the steak seasoning is depicted in Figure 5. The computed intensity curve is compared with the actual measured curve by the tester. The weighting factors and local fractions are adjusted to reduce the error between the two curves.

The data shown in Figure 5 illustrates the actual measured intensity at one point in the tester and the weighted spectral combination for steak seasoning calculated using the overall spectral weighting factors. The measured curve and the computed weighted linear combination exhibit a good agreement.

Sample mixture spectra resulting in a composition of 27.1% Garlic, 19.7% Black pepper, 4.0% Dill seed, 2.0% Red pepper, and 47.2% iodized Morton® salt.

Figure 5. Sample mixture spectra resulting in a composition of 27.1% Garlic, 19.7% Black pepper, 4.0% Dill seed, 2.0% Red pepper, and 47.2% iodized Morton® salt.

The local concentrations of spices in the steak seasoning mixture were obtained by reducing the error between the calculated spectra using weighted spectral averaging and the actual measured spectra from the reflectance probe. This resulted in the concentration profile data for each of the salt grades as illustrated in Figures 6 to 8.

Segregation test results for steak seasoning with iodized salt.

Figure 6. Segregation test results for steak seasoning with iodized salt.

Segregation test results for steak seasoning with kosher salt.

Figure 7. Segregation test results for steak seasoning with kosher salt.

Segregation test results for steak seasoning with sea salt.

Figure 8. Segregation test results for steak seasoning with sea salt.

Red pepper and dill seed do not show any signs of segregation. By contrast, salt, garlic, and black pepper show some interesting interactions. Of all the salts used, iodized salt has the finest particle size and undergoes sifting segregation with both pepper and garlic. But there also exists some angle of repose segregation between garlic and black pepper.

The finer salt piles up near the top of the pile, whereas the garlic piles up at the bottom of the pile. Moreover, when the size of the salt particle is made larger (kosher salt), it actually increases the magnitude of segregation. It is evident that the angle of repose segregation of the coarser particles leads to more segregation compared to sifting segregation.

The segregation that occurs in a bulk material can be represented in another way to help characterize the segregation of pattern (see Figures 9 to 11). The cumulative concentration of various compounds in the mixture can be computed and normalized by dividing by the average concentration of each component. It would be possible to plot this cumulative data against the radial dimension.

Over the radial distance down the pile, all cumulative concentration data would differ from 0 to 1. Then, a uniform material will give a straight line between 0 and 1. Any deviation from this line would suggest segregation. A positive deviation from this line suggests accumulation close to the top of the pile. A negative deviation from this line suggests accumulation near the bottom of the pile.

The overall deviation from this line is a quantitative measure of segregation. As can be seen in Figures 9 to 11, the worst segregation happens in the case of the kosher salt. The sea salt is the least segregating component. This also minimizes the overall segregation of all components.

Cumulative steak seasoning distribution as a function of radial dimension for iodized salt.

Figure 9. Cumulative steak seasoning distribution as a function of radial dimension for iodized salt.

Cumulative steak seasoning distribution as a function of radial dimension for kosher salt.

Figure 10. Cumulative steak seasoning distribution as a function of radial dimension for kosher salt.

Cumulative steak seasoning distribution as a function of radial dimension for sea salt.

Figure 11. Cumulative steak seasoning distribution as a function of radial dimension for sea salt.

A measurement of six repose angles for each of the materials was also carried out and the maximum repose angle representative of the bulk material was selected (shown in Figure 12). In case the repose angle is the only segregation mechanism with this material, then the materials must segregate down the pile in order of repose angle.

The top of the pile should have more amounts of black pepper, followed by dill seed, then red pepper, garlic, and lastly iodized salt. However, in reality, the dill seed and red pepper do not segregate.

Angle of repose for steak seasoning components.

Figure 12. Angle of repose for steak seasoning components.

This is possibly because of the low percentage of these materials in the mixture. There is not sufficient particulate material to effectively create a unique repose angle; moreover, the repose angle of these two ingredients are close to the median response angle. Consequently, the driving force is too little to bring about the segregation of these two materials.

The black pepper, with the largest repose angle, always exhibits a positive deviation from the cumulative concentration plots, suggesting that black pepper piles up near the top of the pile. This is confirmed by the repose angle measurements.

However, as suggested by the repose angle data, the salt should pile up at the bottom of the pile. Actually, the segregation tests suggest that iodized salt piles up at the top of the pile. This is because the particle size of the iodized salt is finer, making it sift down through the void of coarse particles and deposit close to the top of the pile.

Therefore, the sifting segregation driving force and the angle of repose driving force act against each other, thereby reducing the overall salt segregation. In contrast, kosher salt includes some finer particles with the ability to promote sifting; moreover, the angle of repose of kosher salt is almost equal to that of black pepper.

Moreover, the sifting driving force and the angle of repose driving force boost the piling up of the salt at the top of the pile. This is confirmed by the results of the segregation tester, showing kosher salt to be the worst material used for the steak seasoning mixture.

The repose angle of the sea salt is similar to that of red pepper and dill seed and somewhat smaller compared to that of the kosher salt. The sea salt particle size is larger compared to that of the kosher salt, so there is a decrease in the sifting segregation driving force as well as the angle of repose segregation driving force, thereby rendering sea salt as an ideal component to be used for steak seasoning.

From the above analysis, it is evident that this innovative segregation tester has a vital role to play in product design. It can offer guidance in product selection when multiple segregation mechanisms exist. Of importance is the fact that the ideal mixture still shows some segregation; yet it is the perfect one that can be prepared, with the limitation of changing only one material.

Further research can be carried out to improve the segregation profile by varying other components. Nearly 10 minutes is needed to conduct each segregation test. Therefore, within one or two hours, an engineer can identify the perfect steak seasoning mixture; by contrast, earlier methods have needed days or even weeks to obtain the required data.

The novel segregation tester has the ability to minimize the time needed to produce optimal product recommendations using laboratory scale batches. It is a robust tool to gain insights into the segregation of bulk materials.

Conclusions

The innovative spectral method for measuring segregation described in this article is a valuable tool for improving product design. This test method is a straightforward technology for measuring the potential segregation behavior of current or proposed mixtures. Then, the evaluated segregation tendencies can be applied to improve the mixture by choosing the apt components for combination. This should enable the synthesis of new materials using laboratory scale quantities.

Important information to be noted here is that the segregation of multi-component mixtures is usually caused by multiple segregation mechanisms. The tester has the potential to determine nearly four prominent segregation mechanisms apart from chemical composition and the size of the particles. It also reveals the impact of the segregation of one component on the other components in the system.

The segregation capabilities of all components are interdependent; therefore, when one component is changed, usually the segregation capability of all components in the system changes. This often happens in a complex manner. It is possible to observe these details since the tester can measure several concentration points, thereby offering valuable information and enabling the user to acquire more segregation features compared to conventional segregation testing.

It is evident that this tester is useful for successful product design. However, since segregation magnitude relies on changes in one product and as it is possible to measure these differences, the tester can also be used to obtain quality control information in the plant.

A material sample can be acquired from the plant and placed in the tester, which then measures the material segregation and compares this with the segregation capability of a perfect mixture. Small variations in the shape or size of single components will lead to slight differences in the measured segregation pattern and segregation potential.

One of the ways to achieve successful process control is the potential to directly evaluate the impact of important properties on critical bulk behavior. Where possible, it is essential to use the direct measurements of critical process behavior as the control variable.

In simple terms, if product segregation is an important process behavior to be controlled, then it is substantially more beneficial to directly measure segregation than it is to measure wall friction angle and cohesion to find out the segregation behavior. This tester enables direct measurement of segregation of the product.

References and Further Reading

  1. Jerry Johanson, “Solids segregation: causes and solutions,” Powder and Bulk Engineering, August 1988
  2. J. M. Ottino, and D. V. Khakhar, “Fundamental research in heaping, mixing, and segregation of granular materials: challenges and perspectives,” Powder Technology, Volume 121, Issues 2–3, 26 November 2001, Pages 117–122
  3. Anjani K. Jha, and Virendra M. Puri, “Percolation segregation of binary mixtures under periodic movement,” Powder Technology, Available online 7 May 2009, doi:10.1016/j.powtec.2009.04.013
  4. Are Dyrøy, Morten Karlsen, Gisle G. Enstad, Sunil de Silva, “A system for the reduction of air current segregation in silos,” Handbook of Powder Technology, Volume 10, 2001, Pages 623–630
  5. Y. L. Ding, R. Forster, J. P. K. Seville, and D. J. Parker, “Segregation of granular flow in the transverse plane of a rolling mode rotating drum,” International Journal of Multiphase Flow, Volume 28, Issue 4, April 2002, Pages 635–663
  6. D. V. Khakhar, Ashish V. Orpe, J. M. Ottino, “Continuum model of mixing and size segregation in a rotating cylinder: concentration-flow coupling and streak formation,” Powder Technology, Volume 116, Issues 2–3, 23 May 2001, Pages 232–245
  7. Florence Cantelaube, Daniel Bideau, Stéphane Roux, “Kinetics of segregation of granular media in a two- dimensional rotating drum,” Powder Technology, Volume 93, Issue 1, September 1997, Pages 1–11
  8. S. Massol-Chaudeur, H. Berthiaux, and J. Dodds, “The Development and Use of a Static Segregation Test to Evaluate the Robustness of Various Types of Powder Mixtures,” Food and Bioproducts Processing, Volume 81, Issue 2, June 2003, Pages 106–118
  9. D. McGlinchey, “Quantifying segregation in heaps: an experimental study,” Powder Technology, Volume 145, Issue 2, 27 July 2004, Pages 106–112
  10. William R. Ketterhagen, Jennifer S. Curtis, Carl R. Wassgren, and Bruno C. Hancock, “Modeling granular segregation in flow from quasi-three-dimensional, wedge-shaped hoppers,” Powder Technology, Available online 3 July 2007, doi:10.1016/j.powtec.2007.06.023
  11. Thomas Baxter, James Prescott, “Process Development, Optimization, and Scale-up: Powder Handling and Segregation Concerns,” Developing Solid Oral Dosage Forms, 2009, Pages 637–665
  12. Shu-San Hsiau, Jing-I Wang, “Segregation processes of a binary granular mixture in a shaker,” Advanced Powder Technology, Volume 10, Issue 3, 1999, Pages 245–253
  13. Y.R. He, H.S. Chen, Y.L. Ding, and B. Lickiss, “Solids Motion and Segregation of Binary Mixtures in a Rotating Drum Mixer,” Chemical Engineering Research and Design, Volume 85, Issue 7, 2007, Pages 963–973
  14. David Elya, Sai Chamarthy and M. Teresa Carvajal, “An investigation into low dose blend uniformity and segregation determination using NIR spectroscopy,” Colloids and Surfaces A: Physicochemical and Engineering Aspects, Volume 288, Issues 1–3, 5 October 2006, Pages 71–76
  15. Nicholas Christakis, Pierre Chapelle, Nadezhda Strusevich, Ian Bridled, John Baxter, Mayur K. Patel, Mark Cross, Ugur Tüzün, Alan R. Reed and Michael S.A. Bradley, “A hybrid numerical model for predicting segregation during core flow discharge,” Advanced Powder Technology, Volume 17, Issue 6, 2006, Pages 641–662
  16. Kunio Shinohara, and Boris Golman, “Particle segregation of binary mixture in a moving bed by penetration model,” Chemical Engineering Science, Volume 57, Issue 2, January 2002, Pages 277–285
  17. Sanjay Puri, Hisao Hayakawa, “Segregation of granular mixtures in a rotating drum,” Physica A: Statistical Mechanics and its Applications, Volume 290, Issues 1–2, 1 February 2001, Pages 218–242
  18. Guy Metcalfe, Mark Shattuck, “Pattern formation during mixing and segregation of flowing granular materials,” Physica A: Statistical Mechanics and its Applications, Volume 233, Issues 3–4, 1 December 1996, Pages 709–717
  19. Paul Meakin, Remi Jullien, “Simple models for two and three dimensional particle size segregation,” Physica A: Statistical Mechanics and its Applications, Volume 180, Issues 1–2, 1 January 1992, Pages 1–18
  20. Kunio Shinohara, Boris Golman, Takahumi Nakata, “Size segregation of multicomponent particles during the filling of a hopper,” Advanced Powder Technology, Volume 12, Issue 1, 2001, Pages 33–43
  21. Kerry Johanson, Chris Eckert, Dev Ghose, Millorad Djomlija, and Mario Hubert, “Quantitative measurement of particle segregation mechanisms,” Powder Technology, Volume 159, Issue 1, 2 November 2005, Pages 1–12
  22. V. N. Dolgunin, A. A. Ukolov, “Segregation modeling of particle rapid gravity flow,” Powder Technology, Volume 83, Issue 2, May 1995, Pages 95–103

This information has been sourced, reviewed and adapted from materials provided by Particulate Systems.

For more information on this source, please visit Particulate Systems.

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