Mixing: A Detailed Look at the Factors that Influence Mix Uniformity

Jared R. Froetschner, M.Sc. Marketing Manager

DSM Nutritional Products, Inc. Parsippany, NJ

Introduction Feed manufacturing consists of a series of steps, or processes, in which individual ingredients are combined into a homogenous mix, further processed into customer desired forms (coarse dairy rations, pellets, flakes, etc.), and packaged or delivered, again to the customer specifications. Some work has been done evaluating the further processing stages as these steps can directly influence the nutritional performance of the animal. However, very little has been done regarding the process of ingredient mixing. Therefore, the purpose of this article will be to discuss the mixing process and the variables that effect a successful mix. In addition, the measurement of mix uniformity will be examined in detail. General Mixing Theory Mixing is the step in feed manufacturing where the individual ingredients are combined into their proper ratios and uniformity distributed throughout the entire mass. In principle, mixing is very simplistic. Essentially, by adding energy to the ingredients we allow them to disperse into and between each other. Imagine for a moment a drum that is filled, first, with 50 lbs. of basketballs and then 10 lbs. of baseballs; for the purposes of this discussion, the size of the baseball is 1/5 the size of the basketball. Our goal is to empty ¼ of the “mix” with the intention of having equal numbers of baseballs and basketballs. Is this possible? Using the 1/5 size assumption, theoretically it is possible to obtain equivalent numbers of baseballs and basketballs in a given sample. However, we must mix the drum first. If we choose to add the ingredients, and then sample, then depending on the sample location, we will not meet our goal of equal numbers of baseballs and basketballs. Therefore, we need to rotate the drum a sufficient number of times to disperse the baseballs throughout the mix. While the above analogy is simplistic, it does drive home an important point: mixing is the most important step in the feed manufacturing process; if the ingredients are not properly mixed, then the nutritional quality can not be assured. General Mixer Design Mixers can be divided into three categories: 1) horizontal; 2) vertical; 3) TMR/box mixers. Each mixer has advantages/disadvantages.

Horizontal mixers – horizontal mixers (Figure 1) consist of a horizontal tub that contains a shaft, through the center, with the mixing elements. The center shaft is driven by a flywheel connected to a drive motor via a chain or belt drive. In addition, the drive motor shaft is stepped down via a gear reducer.

The mixing elements generally consist of a paddle or ribbon configuration. The use of a paddle vs. a ribbon configuration is a source of much debate; however, in general, the industry has chosen to move toward a ribbon configuration. Paddle configurations were first utilized in horizontal mixers. The paddle concept utilizes a series of paddles connected perpendicular to the

center shaft via a series of secondary shafts. The paddles essentially lift and fold the ingredients, similar to a shovel lifting a scoop out of a sand pile.

In contrast, the ribbon configuration utilizes an Archimedean screw concept in which a continuous ribbon is connected to the center shaft. The continuous ribbon essentially mixes the feed ingredients by pushing/pulling them throughout the length of the mixer.

By far, the ribbon design is more effective than the paddle design. This is because the ribbon design encourages co-mingling of the ingredients with each other, across the entire batch. In contrast, the paddle mixer only encourages co-mingling in

the locus of the paddle itself; consequently, paddle mixers are notorious for dead spots. Dead spots are locations in the mixer in which no mixing occurs due to a lack of mixing action. This propensity for dead spots requires some modifications to the mixing procedure (Mix Cycle/Time section) to account for the reduced mixing action.

Vertical mixers – vertical mixers are quickly disappearing from the feed mill; however, they are still very common in on-farm feed manufacturing operations. This is due, primarily, to their reduced cost; however, because of their design, vertical mixers do fit nicely into an on-farm feed scenario.

Vertical mixers have changed to some extent over the course of the years. Much of this change has been driven by change in animal feeding operations. Twenty years ago, on-farm feed manufacturing was mainly centered on medium to large independent swine operations. However, with the vertical integration of the swine industry much of the on-farm feed manufacturing has shifted towards the medium to large independent dairyman.

The early designs of vertical mixers consisted of a vertical mixing tub. The mixing element consisted of one or two vertical screws that lifted the feed from the bottom to the top of the mixer. When the feed reached the top, it spilled over and was worked back through the center auger configuration. In concept, the mixing action is effective and it is capable of producing a homogenous mix; however, because of the slow mixing action, the amount of time required to achieve a homogenous mix is considerable longer (up to 5x) than the horizontal ribbon design.

The next generation of vertical mixers, primarily designed for the dairy, utilizes the same center auger concept, but also includes knife blades on the auger flightings to produce some degree of particle size reduction in the coarse, roughage fraction.

TMR mixer - The final type of mixer that is

commonly utilized in the United States is a TMR mixer. While the argument can be made that the next generation of vertical mixers are TMR mixers, the traditional TMR mixer is more of a truck mounted horizontal mixer. The mixing elements in these mixers consist of two horizontal, intermeshing augers mounted along one of the sides of the box. Off-set from the center of the box is a horizontal shaft with three horizontal bars. This type of mixer is very common in feedyard settings and is also becoming more prevalent in dairy operations (commodity barn and on-farm manufacturing). While the

vertical mixer and the TMR mixer both utilize different principles of action, they both serve a common purpose: the efficient mixing of rations that contain high levels of coarse forage. Traditional ribbon or paddle horizontal mixers are not that effective at diets that contain roughage. This is evidenced by the hesitancy to include whole cottonseed in the feed manufacturing process. Instead, horizontal ribbon and paddle

mixers are designed to mix ingredients with a fairly similar particle size, quickly and efficiently.

On the other hand, vertical and TMR mixers are designed to blend coarse forage particles with other coarse ingredients, such as beet pulp and whole cottonseed. Keeping this concept in mind, it is very easy to see that the TMR or vertical mixer is not designed to effectively mix ingredients with a very small particle size or are included in very small proportions in the ration. Therefore, the concept of pre-mixing some ingredients provides the necessary transition between horizontal feed mixing and on-farm ration mixing. Effect of Mixer Components on Mixing Mixers are mechanical devices. Therefore, wear is associated with mixers and attention should be paid to the physical components of the mixer. These components include: mixing elements, tub liner, liquid addition ports, and the discharge gate.

Mixing elements – the heart and soul of horizontal mixers (or any mixer for that matter) are the mixing elements. The mixing elements consist of a center support/drive shaft and the actual components that provide the mixing action. In general, these components are either ribbons or paddles. Ribbons – ribbons are more resistant to wear than paddles, since the mixing load is distributed evenly across then entire circumference of the ribbon. However, this resistance to wear is also one of the key checkpoints for ribbon mixers in that they should be inspected on a monthly basis to determine wear patterns. Ribbons are also sensitive to broken flighting concerns, especially with high mineral or high liquid loads. Because of their design, the leading edge of the ribbon stays fairly clean, but the backside of the ribbon and the center shaft need to be cleaned on a frequent basis; especially if liquids are added to the mixer.

Paddles – paddles are very sensitive to wear. The increased propensity for wear is due to the constant and direct contact of the individual paddle amongst the feed. In contrast, a ribbon varies contact due to constant rotation; however, a paddle does not have that luxury and is therefore in constant contact with abrasive feed particles. Therefore, a paddle mixer should be inspected monthly to determine which paddles are worn and require replacement. The advantage of a paddle mixer is that the mixing elements can be changed on an individual basis, in contrast to the ribbon mixer, which requires an entire ribbon change. The paddles have several wear points of concern; these wear points will vary by make and model of mixer, however, for the purposes of this discussion, the focus will be on Marion paddle mixers. The attached photo displays the backside of a Marion paddle.

The points of concern on this paddle are the tips (red arrows) and the width of the paddle from the top to the bottom, in the center (green arrow). In general, the Marion paddle will demonstrate wear on the right tip first; once this tip is worn, the center width (green arrow) will begin to wear. This wear point is the most significant as it affects the distance of the paddle from the mixer shell. The

gap between the mixing element and the mixer shell is the key determinant in wear of a mixing element; if the gap is excessive (x > ½”), mix quality will be affected substantially. While the center width is wearing, the edge points also begin to wear. Consequently, the paddle begins to take on a more defined parabolic shape along the top, leading edge. When this occurs, the gap between the shell and the mixer paddle becomes uneven, which leads to poor mixer performance and feed buildup. Table One displays a wear point analysis of the actual mixer paddles from the mixer in the previous picture. Table 1. Marion paddle mixer paddle analysis

Analysis Date Blade New May Septembera

90°

Width, in. 6.500 6.000 6.375 Length, in. 18.625 18.500 18.375 Shell clearance, in. .25 .500 1.250

180° Width, in. 6.500 6.500 6.375 Length, in. 18.625 16.750 18.250 Shell clearance, in. .25 .190 .281

270° Width, in. 6.500 6.250 6.500 Length, in. 18.625 18.250 18.500 Shell clearance, in. .25 .500 .250

aBlades were changed the first of June, 2003. The shell clearance category will be dealt with at a later point, as there are other concerns/solutions to this problem. The key to any analysis is to examine a new paddle alongside the current paddles being examined. In this case, three paddles were examined and two salient points emerge: the wear is occurring along the width transversal and the length as well. Therefore, the take home point here is that even though the width is still within specification, if the length is significantly shorter than the new paddle, then the paddle should be changed. Again, the wear on this design of paddle occurs in two places and the paddle should be analyzed in this context. Analysis of this mixer revealed that the length dimension (horizontal, point-to-point) was the cause of the most concern, especially in the paddle in the 180° position. Consequently, based on the excessive build-up in the mixer and the dimensional analysis, it was suggested that the entire paddle configuration be changed to improve mixer performance.

Mixing Element/Mixer Shell Clearance – going hand-in-hand with the blade analysis is the gap between the mixer shell and the mixing element. In this discussion, the differentiation between paddle vs. ribbon can be ignored, as the only difference is the method of adjustment. A good rule of thumb is that when the gap between the shell and the mixing element exceeds ¼”, then the mixing element should be adjusted to decrease this gap. For ribbon mixers, this is accomplished by removing individual sheaves in the main shaft bearing mount. For paddle mixers, the individual paddle is moved closer to the shell by adjusting the location of the paddle bolt in relation to the support shaft; all paddles have an oblong bolt hole that allows for some degree of adjustment, closer or further from the mixer shell. It is important to note that whenever adjustments are made

to the ribbon or paddle, care should be given to the uniformity of the mixer shell or any deformations in the center shaft. Consequently, this adjustment should be undertaken by competent maintenance personnel who know the physical condition of the entire mixer. If this is ignored, severe damage to the mixer can occur as metal-to-metal contact will ensue.

Mixer tub liner – the mixer tub liner should be inspected every 6 months for signs of excessive wear. In general, the warning signs are ridges and valleys that occur with improperly adjusted ribbons and paddles. This occurs primarily by ignoring the gap between the mixing elements and the tub. When the gap becomes too excessive, the feed flow pattern becomes layered. When this occurs, any movement of the bottom feed layer is due to friction imparted by the mixing element. Over time, this excessive friction will cause wear patterns to develop in the tub surface, which will increase the incidence of wear. Similar to a pellet mill door, the uneven wear of a mixer liner is an expensive problem that can be, to a large extent, minimized by routine preventative maintenance.

Liquid addition – in today’s feed manufacturing environment, liquids are routinely added to the mix. These liquids can include: water, molasses, fat, and liquid amino acid sources. Proper maintenance/design of the liquid addition system is key, as ignorance of the issues can lead to excessive build-up, ingredient carry-over, and increased mixer liner wear. The attached picture demonstrates this issue very well. The

liquid addition system for this mixer consisted of two copper pipes with a crimped discharge end. This “arrangement” was responsible for excessive build-up along the walls; furthermore, the actual amount of liquid that was added to the mixer could not be verified, as it was strongly suspected that a portion of the liquid did not make it to the feed, but was instead entrained in the build-up. Consequently, some changes to the addition system were required to facilitate uniform liquid application. One change that can be attempted would be to fit the addition line with a spray nozzle to facilitate some degree of backpressure in an attempt to get the liquid away from the side of the mixer walls. In addition, the use of the spray nozzles would allow for a pattern to be applied to the stream, which would facilitate more uniform liquid distribution throughout the mix.

Another issue with this liquid addition system was the use of copper piping. The use of copper piping with animal fat is strongly discouraged, as copper will react with the fat and cause rapidly accelerated oxidation of the fat, which can lead to off-flavors. This is especially important in swine diets, as pigs are very sensitive to these off-flavors.

Discharge gate – the most overlooked and ignored component of the mixer is the discharge. Proper maintenance and function of the discharge is essential to insuring that all ingredients have an equal opportunity to co-mingle with one another. Mixer discharges can be classified in two

categories: gate and drop bottom. Gate discharges are essentially comprised of a port installed in the bottom of the mixer that typically has a movable slide installed over the mouth of the port. When the mix cycle is complete, the gate is opened (via pneumatic or hydraulic means, primarily) and the feed is discharged into a conveyor below the mixer.

In contrast, drop bottom discharges are composed of two doors that also function as the mixer tub liner. When the mix cycle is complete, the doors are opened and the entire batch of feed is dropped into a surge bin installed between the mixer and the take-away conveyor.

From an efficiency stand-point, drop bottom discharges are the most efficient and allow for maximization of the mix cycle. However, the drop bottom design is also more prone to the one major concern with the discharge: ingredient leakage. One of the issues that will be dealt with in more detail is density of the individual ingredients. For this discussion, however, it is important to realize that the ingredients in the mix have a wide range of bulk densities. As a result, heavier ingredients have a tendency to settle during the mixing cycle, compared to the lighter ingredients (cobalt carbonate vs. wheat midds). Because of the difference in ingredient densities, there is a propensity for greater density ingredients to settle towards the bottom of the mix, especially if the mixing elements are not functioning as designed. If the discharge gate is completely closed, or if gaps exist along the run of the drop bottom discharge, ingredient leakage can occur. Therefore, some attention must be paid to ensure that when the discharge gate/doors are closed, that the seal is tight and ingredient leakage is not occurring. Influence of Ingredient Characteristics on the Mixing Process Previously, mixing was compared to an analogy using basketballs and baseballs. This analogy is a great launching point for a discussion concerning ingredients and their effect on the mixing process. While the nutritional characteristics of ingredients is always a good discussion issue, the purpose of this discussion is to examine the physical characteristics of the ingredients, as these characteristics primarily effect the mixing process; of interest in this discussion is ingredient density, particle size, and particle count. Ingredient density – a simplistic definition of bulk density is the quantity of a given ingredient that can fit into a given space. For example, corn has a density of 56 pound per bushel; therefore, 56 pounds of corn will fit into a leveled, bushel basket. The influence of ingredient density on the mixing process must be taken into account. Mixers, by nature, are volumetric; consequently, the density of the ingredients plays a critical role in regard to mixer performance. It is common industry practice to refer to a mixer’s capacity in terms of weight; however, this practice can be very misleading and ignores the issue of degree of fill in the mixing volume.

If ingredient density is ignored, then the probability of overfilling or underfilling the mixer is a distinct possibility. From a process standpoint, the issue of over- or underfilling results in the same problem: poor mixing element performance due to excessive or minimal degree of fill. From the commercial feed manufacturing perspective, overfilling is more common. This is due to the use of wheat midds and other “fluffy” ingredients as primary components of the mix. If the density of the “mix” is ignored, then it is possible for the very light ingredients to render the mixing elements useless as they cannot efficiently move the bulky mass in the normal mixing pattern. Instead, the top portion remains motionless while the lower regions of the mix follow the standard mixing pattern. Conversely, when the ingredient density is very heavy (e.g. mineral mixes), the required batch weight may not fill enough space in the mixer for the mixer elements to do an effective job of mixing the ingredients. In general, a couple of rules of thumb: for light fluffy mixes (density < 37.5 lbs/bu), adjust the batch weight to achieve a degree of fill of no more than 65%; for very dense products (density > 65 lbs/bu), do not attempt to mix if your degree of fill is less than 30%. This last point is very critical. Occasionally, these mixes are attempted by adjusting the weight to achieve the desired degree of fill. This is not wise as the amount of weight required for very dense mixes may be greater than the load allowed on the mixer shaft. Particle size / particle count – a discussion on mixing without considering particle size would be incomplete. Particle size is key in this discussion due to that fact that most of the ingredients in traditional feed manufacturing have undergone some degree of particle size modification. Particle size, in regard to mixing, can be either smaller or larger than the beginning substrate. However, for purposes of this discussion, it is assumed that the particles will be made smaller than the substrate. Reducing particle size is accomplished through grinding. The grinding process relies on either a hammermill or a roller mill to take the original particle and reduce it to the desired particle size. When we reduce the particle size, it is of definite interest to the mixing process to quantify the change due to grinding. This has largely been ignored in the literature except for isolated references with no attached data. At this point, it becomes necessary to discuss particle size analysis, to some degree. Currently, there are two widely used techniques for particle size analysis: sieve analysis and laser diffraction. Laser diffraction is more common in the chemical and pharmaceutical industries, as this tool is very effective at quantifying particle size of liquids and other dispersants necessary for emulsions and chemical solutions. However, laser diffraction is finding a home in the dry chemical field in regard to paint dispersants and other particle size dependent processes. Laser diffraction truly excels with ingredients that have a very uniform particle size distribution. If the distribution is fairly uniform, the resolution is down to the .1 μm (.0001 mm) range. In the feed industry, sieve analysis is the method of choice. Sieve analysis relies on the quantity of a given sample that is retained on the top of a selection of screens. The benefit of this analysis is that it is robust enough to deal with the fairly non-uniform particle distributions found in animal feed manufacturing. Furthermore, a sieve analysis allows the user to define the screen sizes that are important in their process. The

drawback to sieve analysis is that the screens only provide a range of particle sizes upon which to quantify the particle size distribution.

To contend with the problem of the non-uniform distribution and the sieve screen range issue, Pfost and Headley (1976) developed a method to quantify particle size using a log-normal distribution. This test, ASAE S319 allows the user to measure the average particle size (Dgw) and the uniformity of the particle size (Sgw). This method is wonderful for the great majority of feed ingredients; however, it is not a good method for the determination of ingredients with very large (e.g. hay) or very small (cobalt carbonate, Rovimix H-2) particle sizes. Particle size is very important when the process of mixing is considered. Table Two highlights the particle size analysis of several common feed ingredients. It is important to note

that the values presented in Table Two are single point observations. The particle size analysis may vary considerably by supplier and sampling location. Instead, the purpose of Table Two is to visually present the issue of particle size in regard to mixing. Table 2. Particle size analysis of common feed ingredients Response Criteria Ingredient Dgw Sgw Particle count, #/gram Corn 1125 1.9 3,763 Soybean meal 974 2.1 9,300 Wheat midds 449 1.5 18,032 NaCl 498 1.4 5,227 ZnSO4 730 1.4 1,909 MnSO4 614 1.4 2,766 CaCO3 559 2.5 118,870 CoCO3

a 3.0 - 2,000,000 Rovimix Biotina 60 - 20,000,000 aObtained using laser diffraction analysis. As can be seen, the average particle size of the ingredients can vary tremendously. Recall, 1 mm is approximately 1000 μm. Therefore, the particle size of the ingredients in a common feed mix can vary tremendously. But why is particle size important? The differences in particle size allow us to mix much more efficiently. For example, if the goal was to mix 1 blue basketball into 5000 red basketballs, how much confidence would one have that the target recipient would have an equal chance of finding the blue basketball. On the other hand, if the 1 blue basketball was equivalent to 100 blue baseballs, it is very easy to make the connection that the probability of finding a blue baseball is significantly greater than finding the blue basketball. The above discussion lends well to the particle count issue. Examination of Table two demonstrates a very salient point: significant differences in particle size also lead to

significant differences in particle count. This fundamental concept is very critical in mixing theory, as significant differences in particle count can lead to fundamental differences in the distribution of the ingredients throughout the mix. One of the trends in animal nutrition is the push to include micro-nutrients into the daily rations of animals. These micro-nutrients can be defined as any ingredient that goes in at rates lower than .5% in the mix; however, it is not uncommon to find ingredients that are being included at rates lower than 100 ppm. Because of this trend to feed the animal on the micro-level, it is absolutely critical that the micro-ingredients are uniformly distributed throughout the entire mix. For example, consider biotin; biotin is typically included in the ration at very low levels. Consequently, it is very important that the biotin is distributed throughout the mix, so that the animal has an equal opportunity to receive the biotin it requires in each and every bite. Table Three illustrates this point very nicely; with broiler chicks, considering that the daily feed consumption is Table 3. A comparison of biotin intake by broiler chicks consuming two sources of biotin Product Category Rovimix Biotin Competitive Triturate Inclusion rate, mg/ton 100 100 Biotin consumption, particles/day 1000 12 very low, it is absolutely essential that the micro-nutrient have a very large particle count, so that the animal is assured that, in each and every bite, it has an equal opportunity to ingest the proper amount of active nutrient.

Analogous to the particle count discussion is uniformity. From a practical stand-point, the more uniform an ingredient is, the greater the chance the ingredient will be distributed uniformly throughout the mix. The attached pictures compare Rovimix H-2 (left

picture) to a competitive triturate product (right picture). Examination of the pictures demonstrates an important point: using the product on the left insures that each particle delivered to the animal contains biotin; in contrast, each particle, in the picture to the right, delivered to the animal may be biotin, or it may be a carrier. This area of discussion is not complete without covering the area of premixing. Premixing is the practice of taking very small inclusion micro-ingredients and mixing

them with a nutritive carrier to “pre-disperse” the micro-ingredients in, with, and under a carrier. The completed premix is then added to the feed at the desired inclusion rate. The primary advantage of premixing is that the end user only has to mix the premix carrier into the finished feed; the micro-nutrients have already been successfully mixed into the carrier. Therefore, if the premix is uniformly distributed throughout the mix, it can be assumed that the micro-ingredients are also uniformly distributed. One of the primary reasons for the improvement of micro-ingredient delivery is that the use of a premix can significantly alter the physical properties of the “ingredient” added to the mixer, many times to benefit of the end-user. Table Four presents a comparison of the physical properties of Rovimix A-500 and a common premix, an A-30 dilution. Table 4. Physical properties of Rovimix A-500 and an A-30 dilution premix Product Response Criteria Rovimix A-500 A-30 Dilution Bulk density, g/cc .50 .58 Dgw 225 459 Particle count, particles/g 4,698,151 628,541 Heubach dust index, mg/100 g 71 16 Flowability 7.0 6.5 Table Four is instructive for a couple of reasons. The first reason is that it clearly shows that the use of a premix can improve some of the more important handling properties of a micro-nutrient, such as bulk density and dustiness (Heubach dust index). Secondly, Table Four demonstrates that the use of premix helps minimize the difference in particle size between ingredients. Previously, it was suggested that large differences in particle count and particle size can make a tremendous difference in the mixability of an ingredient. Taking this concept a step further, a premix allows the end-user the flexibility of uniformly pre-distributing several micro-nutrients at once and then, only having to worry about dispersing the premix into the desired finished product; the end-user no longer has to worry about the micro-ingredients, as they have already been “pre-mixed” into the finished product! The Mixing Process The mixing process consists of several vital process components. These include: Mix cycle, mix time, tip speed, and mixing element design. Mix cycle – the mix cycle consists of a dry mix time and wet mix time. This differentiation is very important. As a general rule of thumb, the mixing process requires that the dry ingredients be added to the mixer and mixed for a predetermined amount of time. This is due to the fact that liquids can reduce the dispersiblity of dry ingredients within each other by coating the dry particles with liquid. On the other hand, the wet mix time is required to efficiently disperse the liquid throughout the mix. This dispersion is necessary as some liquids (i.e. molasses, fat) have the tendency to clump. Therefore, the uniform distribution of the liquids throughout the mix cannot be assured. Table Five presents data collected during a mix uniformity trial conducted for a large layer account. The account in question did not have a formal dry mix time; instead,

the liquids were added immediately after the addition of the last dry ingredient. The wet mix time was 2.5 minutes. The results indicate a poor mix uniformity for the mixer in Table 5. Mix uniformity of a mixer with no dry mix time Response Criteria Value Batch variance 70.10 Mix uniformity test

Target Na, % .17 Actual Na, % .15 Standard deviation .03 Coefficient of variation, % 21.61

question, with a coefficient of variation (C.V.) greater than 21%. Mix time – probably the most controversial area of discussion regarding the mixing process is the topic of mix time. Without a doubt, most feed mills with a solid reputation agree on the need for a separate dry and wet mix time. What they disagree on is the length of the dry/wet mix. This issue is necessarily intertwined with the manufacturing costs of the feed plant. A 1 minute change in the dry mix time of a plant can have a significant effect on the overall capacity and efficiency of a plant. For example, assume a plant is currently manufacturing 3 tons of feed on a 1 minute dry mix time; if the dry mix time is increased to 2 minutes, this results in a loss of 8000 tons of feed per year. Even though the mix time issue is intertwined with the economic status of the plant, it is an issue that must be addressed, especially if the uniformity of the mix needs some improvement. Many times, when the uniformity exceeds acceptable limits (Table 6), the only option left to a feed manufacturer is to increase the overall mix time. Table 6. Mix uniformity guidea C.V., % Rating Corrective Action < 10 Excellent None 10-15 Good Increase mixing time by 25-30% 15-20 Fair Increase mixing time 50%; look for worn equipment,

overfilling, or ingredient sequencing > 20 Poor Possible combination of all the above. Consult

extension personnel or feed equipment manufacturer aAdapted from Herrman and Behnke (1994). Many times, the equipment manufacturer provides solid advice regarding the appropriate dry/wet mix times for the make and model of mixer installed. However, it is important to note that these times may be adjusted up or down according to product mix and production requirements. As a note of caution: the batch cycle needs to be considered any time that the mix cycle is changed. Many times, the batch cycle is synchronized with the mix cycle to insure that the ingredients are scaled in time for the mixer to complete its

mix cycle and discharge. A dramatic shift in mix time, in either direction, can have additional ramifications to production efficiency, irrespective of the mix cycle. Table 7. Recommended mix times by mixer type Mix Time, min. Mixer Type Dry Mix Wet Mix Paddle 3 3 Twin shaft, double paddle .5 1 Ribbon 2 3 Double ribbon 1-2 2-3 Twin shaft, twin ribbon .75-1 2 Vertical tub mixer 5-10 5-10 TMR mixer 5-10 5-10 Tip speed – if a mixer has a poor uniformity result and changing the mix times is not an option, the speed of the mixing elements at their tips can be increased. There is limited data in the public domain regarding this issue (Table 8); however, most mixer manufacturers can assist regarding the proper balance between tip speed and mix uniformity. Table 8. Effect of mixer shaft revolutions on uniformity of a broiler dieta Main Shaft Revolutions, RPM Item 5 20 80 Coefficient of Variation

Salt, % 40.5 12.1 9.7 Red micro-tracer, % 53.4 16.6 11.3 Blue micro-tracer, % 53.9 17.0 10.6 Sodium, % 44.5 23.2 22.8

aAdapted from McCoy et al., 1994.

Mixer/mixing element design – occasionally, a change in the mixing element or mixer design may be necessary to achieve a good, uniform mix. This decision is not one to be made lightly; many times, this modification requires a substantial capital investment. However, occasionally, this decision must be made. Table Nine presents data collected from two mixer designs (paddle vs. twin shaft, twin ribbon) using the same formula composition and tracer. As can be seen, a major difference exists in dry mix time between the two mixers. Furthermore, the customer with the paddle mixer cannot increase the dry mix time as this would have deleterious effects on the daily production rate of the plant. Table 9. Mix uniformity comparison of two mixer designs Mixer Design Item Paddle Twin Shaft, Twin Ribbon Dry mix time, min. 3.50 .75 Target Co, ppm 100.0 100.0 Actual Co, ppm 72.5 74.6 C.V., % 16.2 5.52

Mix Uniformity Earlier in this document, the concept of mix uniformity has been introduced. The issue was not developed because it requires a section of its own. Therefore, the issue of mix uniformity will be examined in the context of measurement, interpretation, and effects on animal performance. Measurement – the measurement of mix uniformity is a much debated issue. However, a standard method does exist. The standard method is ASAE S303.1. This method provides a routine methodology for testing the performance of a mixer. The standard method was developed from work completed by Pfost (1976) and relies on the analytical measurement of a known marker placed in the feed.

Essentially, mix uniformity is determined by adding an ingredient that provides a necessary nutrient that is not found in great abundance in the diet, with the exception of the added ingredient. Once the ingredient is added to the mix, the diet is mixed according to the standard mix procedure and the mixer is then sampled. Ideally, ten samples are collected from ten, independent sampling locations, identified prior to the test. The sampling locations should be

uniform across all tests. For example, if sample location one is located on the right hand side of the drive end, then this location should be maintained for all tests conducted on this mixer. This allows for analysis of uniformity by sampling location, across test. As a general rule of thumb, a linear pattern blocking the mixer into 10 sampling zones is the most effective. However, this can vary based on the physical size of the mixer and the number of samples required to give a statistically meaningful result.

The marker utilized in the test should be one that contributes as much of the target nutrient as possible, with very little interference from the rest of the diet. For example, Chloride is a poor choice as a marker because it is present in many feed ingredients; on the other hand, Cobalt is a wonderful choice because it is found in very low levels in most feed ingredients. While the endogenous content of the marker is of a concern, so is the analytical

variability that accompanies analysis. Pfost (1976) stated that the uniformity of a mix consists of several variance components. These components can include: batching weights, mixing uniformity, sampling variance, and analytical variance. Consequently,

prior to conducting a mix uniformity test, all of the sources of variance need to be documented and either quantified or eliminated to strengthen the test and allow the researcher the opportunity to discern the true uniformity of the mix. Earlier it was suggested that a mixer be sampled at the mixer. It has become common practice to sample mixers at points further down the process. Unless absolutely necessary for safety reasons, it is strongly suggested that the samples pulled for a mix uniformity test be taken at the mixer. If the samples are taken at a location other than the mixer itself, then the sample size will need to be increased to strengthen the test by increasing the degrees of freedom in the calculation of mix uniformity. Interpretation of results – once the samples have been collected, submitted for analysis, and results have been returned, the next step is interpretation. Assuming that the variability in the analytical procedure is known, the ultimate goal is to determine the coefficient of variation between the collected samples. The first step is to quantify what distribution best describes the numerical data. The accompanying distribution is critical to the proper calculation of the coefficient of variation.

The figures to the left illustrates two, independent sets of data that illustrate two different distributions. The top figure illustrates a normal distribution and the bottom figure illustrates a non-normal (double-peaked) distribution. Distribution analysis is critical to the proper calculation of the uniformity. Pfost (1976) proposed that the uniformity of a

mix can be best described by calculating the coefficient of variation (C.V.). The C.V. relates the standard deviation to the mean in the form of a percentage. Said another way, the C.V. is essentially a method to express the sample standard deviation as a percentage of the sample mean. In general, in properly categorized distributions, the standard deviation and the mean change together. Therefore, the C.V. is an effective tool for describing the variability present in the sample population

(Snedcor and Cochran, 1967). The general equation for the C.V. is as follows:

100.,%. ×⎟⎟⎠

⎞⎜⎜⎝

⎛= −

x

sVC (eq. 1)

Normally Distributed Data Set

0

1

2

3

4

5

6

76 82 88 More

Co, ppm

.00%

20.00%

40.00%

60.00%

80.00%

100.00%

120.00%Frequency

Cumulative %

Binomial Distribution

0

1

2

3

4

5

6

7

0.11 0.14 0.17 More

Sodium, %

.00%

20.00%

40.00%

60.00%

80.00%

100.00%

120.00%

Frequency

Cumulative %

where:

s = sample standard deviation

averagesamplex =−

The general equation for the C.V., as can be seen above, is dependent on the accurate calculation of the sample standard deviation and the sample average (or mean). Consequently, these calculations are strongly dependent on the classification of the data distribution. In general, two data distributions are commonly observed in mixer testing. These include the normal and non-normal distributions. Normal distributions – equations 2 and 3 can be used to calculate the sample average and sample standard deviation for data that approximates a normal distribution.

nx

x i∑=−

(eq. 2)

1−

⎟⎠⎞

⎜⎝⎛ −

=∑

−

n

xxs

i

(eq. 3)

These equations are dependent on the data assuming a normal distribution. Therefore, the data must be categorized prior to making these calculations. One area that deserves some mention is the determination of outliers in a data set. The presence of outliers in a data set can skew a distribution and can be responsible for faulty data analysis. One of the best tools to test for the presence of outliers in a data set is the Kurtosis value. Kurtosis measures the degree of departure from normality present in a given data set. The general equation for the kurtosis of a population is given in equation 4:

( )4

4

σμ−

= ixk (eq. 4)

Equation 4 is a good general equation to demonstrate the concept behind kurtosis; however, this equation is only good for data sets with n ≥ 100 observations. The equation for data sets with much smaller sets of observations is very complicated and beyond the scope of this paper. However, the value is very easily calculated using the Univariate analysis functions of most spreadsheets and statistical packages. As stated previously, kurtosis provides the end-user with the ability to spot an outlier in a given data set. In general, if the kurtosis is greater than ⏐3⏐, there is a significant probability that an outlier exists in a data set. Typically, if an outlier exists, the kurtosis will be significantly greater than ⏐3⏐. Non-normal distributions – the normal distribution, by definition, is a probability distribution. The defining characteristic of a normal distribution is a single, defined peak. When a data set does not contain a single peak, but instead has a double peak, the

probability distribution is no longer normal and cannot be represented by the equations that govern a normal distribution. If this situation is encountered, the best tool available is to calculate the frequency distribution for the data and interpret the results in light of the probability that a given observation will occur. Table Ten highlights this approach to determining the uniformity of a mixer in which the results indicate that the data is behaving in a non-normal fashion. Table 10. Analysis of a non-normal mix uniformity data set Item Value Target Na, % .15 Distribution analysis, %

< .11% 13.33 .12-.15% 40.00 .16-17% 20.00 >.17% 26.66

Using this approach, it is apparent that 40% of the observations fall within an acceptable range of variability. However, 60% fall outside of the range. As can be quickly noted, the use of this approach requires some understanding of the acceptable ranges in a system. Furthermore, the data must be categorized prior to analysis to allow for the determination of the frequency distribution. Sample size is a critical issue to consider, especially if the distribution is suspected to be non-normal. The industry average in regard to the appropriate number of samples is ten. However, if non-normality is suspected, that number should be increased to at least 15; twenty is a more reliable value. An alternate approach that can be used to determine a C.V., is to make a normal approximation to a binomial distribution. Using the distribution analysis above, it is apparent that 40% of the observations fell within the desired range of values. Therefore, a C.V. can be calculated using equations 5 and 6 and applying the values to equation 1.

πnx =−

(eq. 5) where:

n = number of observations π = probability that a sample will result in the desired value

( )ππ −= 1ns (eq. 6)

Analyzed using equations 2 and 3, the C.V. for this set of data was found to be 22%. When the binomial equations were applied, the C.V. was found to be 32%. This difference is significant enough to change the entire interpretation of the data set. The above example clearly illustrates the importance of quantifying mixer performance correctly. Many times, 10 samples are obtained from the mixer, and are tested for an analyte that has been added at a relatively high inclusion rate. Following this approach and neglecting to test for ingredients at critical inclusion levels can result in a misleading mixer profile. As a general rule of thumb, a mixer should be tested for

uniformity using a tracer at the lowest ingredient inclusion rate utilized in the plant. Using this approach can help quality and production managers adjust appropriate mixing variables to achieve a consistent mix. Conclusion Mixing is one of the most critical operations in a feed mill. Factors such as equipment type/condition, ingredient size, and mix time can significantly alter the uniformity of the mixed product. However, a recognition of these critical factors can help maintain acceptable mix C.V’s, which will result in the delivery of nutritionally uniform diet to the animal.

Literature Cited

American Society of Agricultural Engineers. 1976. Test Procedure for Solids – Mixing Equipment for Animal Feeds. In: H.B. Pfost (Ed.), Feed Manufacturing Technology. AFMA, Arlington, VA. pp. 521.

Herrman, T. and K. Behnke. 1994. Testing Mixer Performance. MF-1172. Kansas

State University Agricultural Experiment Station and Cooperative Extension Service Bulletin. Kansas State University, Manhattan, KS.

McCoy, R.A., K.C. Behnke, J.D. Hancock, and R.R. McEllhiney. 1994. Effect of

mixing uniformity on broiler chick performance. Poult. Sci. 73:443. Pfost, H.B. 1976. Feed Mixing. In: H.B. Pfost (Ed.), Feed Manufacturing Technology.

AFMA, Arlington, VA. pp. 85. Pfost, H.B. and V. Headley. 1976. Methods of determining and expressing particle size.

In: H.B. Pfost (Ed.), Feed Manufacturing Technology. AFMA, Arlington, VA. pp. 512.

Snedecor, G.W. and W.G. Cochran. 1967. Statistical Methods. Iowa State University

Press, Ames, IA.