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Ingredient density affects feed mill efficiency

Ingredient density affects feed mill efficiency

This article discusses challenges associated with utilizing low-density alternative ingredients as well as potential ways to gain back lost efficiencies in the feed mill.


*Dr. Charles Stark is the Jim & Carol Brown associate professor in feed technology at Kansas State University. Dr. Leland McKinney is with DFS Inc. in Johnston, Iowa. Dr. Adam Fahrenholz is assistant professor in the Prestage department of poultry science at North Carolina State University.

HOW many tons of feed are being produced at each of your facilities? This is a common question asked and is largely the way people in our industry communicate capacity and production efficiency.

However, a more appropriate question might be: How many cubic feet of product are being produced at a particular facility? This is because feed mills are designed based on volume, not weight, which becomes very evident when low-density alternative ingredients are priced into formulas and, in many cases, begin making up a substantial portion of the diet.

Feed mills that are designed to manufacture primarily corn/soybean meal-based diets have had to gain a better understanding of how to efficiently utilize low-density alternative ingredients and adapt to the situation.

The purpose of this article is to discuss challenges associated with utilizing low-density alternative ingredients as well as potential ways to gain back lost efficiencies in the feed mill.


Measuring density

Before discussing how low-density alternative ingredients affect processing, it is worth reviewing briefly how density is determined.

The Federal Grain Inspection Service has standard methods for determining the test weight (density) of cereal grains. However, there are no standard methods or equipment in the feed industry for determining the density of all feed ingredients.

Basically, all that is required is a container of a known volume, the test product and a scale to weigh the container full of the product. Note that the product must be flush with the top of the container. Once the weight of the product and volume are determined, the density can then be calculated by dividing the mass by the volume.

A considerable amount of variability can exist in density results. Factors that may contribute to the variations are: (1) test repeatability between operators/staff, (2) the distance the product drops into the container, (3) the potential of packing the product in the container during striking and (4) the container size.

We conducted an experiment (unpublished) to quantify how container size — comparing a 1 cu. ft. box, a 0.25 cu. ft. box and a 100 mL graduated cylinder — affected density results. We measured the density of multiple samples of soybean meal, dried distillers grains plus solubles (DDGS) and wheat midds.

The results were consistent across the ingredients tested in that as container size increased, density results increased in a parallel manner. This was expected due to the compaction caused by the larger mass of the sample.

As the container size decreased, the standard deviation increased, suggesting that a larger-volume container yields more consistent results. However, the disadvantages of using the larger-volume container were the effort, time and sample size required to obtain the density measurement.

The smaller container (100 mL graduated cylinder) was more convenient, leading to long-term consistency in obtaining the information as heroic efforts fade over time and result in an absence of data.

Another set of data collected during a feed technology laboratory at Kansas State University also illustrated the differences in product density of whole corn, ground corn and soybean meal measured with four methods using a 1 cu. ft. container. The greatest increase was between the loosely packed density and vibrated or compacted density (Table 1).

It is also interesting to note that the percent difference varies among products, especially whole grains versus ground products.

Ingredient density test results can be used to predict the densities of complete feeds and to make decisions regarding batch sizes and pelleting parameters.

Fahrenholz et al. (2013) determined the density of mash and pelleted feeds that contained 0%, 10%, 20% and 30% DDGS (Table 2). There was a linear decrease in the density of both mash and pelleted feed, but the effect was more pronounced in the mash diets.


Balance batching and mixing

In a perfect world, the batching time, mixing times (dry mix, liquid addition and wet mix) and surge/take-away equipment would be in complete harmony or balance. However, this is rarely the case, particularly as densities fluctuate between formulas.

In some cases, the mixer is waiting on the low-level indicator in the surge. Other times, the scales are waiting on the total mix cycle to reach completion, or the mixer is waiting on the batching process.

Nonetheless, it is to your advantage to understand cycle times for each formula and how density may be affecting constraints.

Questions each manager should ask are:

* How many screw feeders are drawing a particular ingredient per batch?

* How are the feeder screws positioned relative to the scale, e.g., is there enough vertical space to add an ingredient without it backing up into the transition?

* Has there been a review of freefall settings, settle times and cutoff speeds?

* Are the surge conveyor, leg and bin spouting sized to accommodate the volume and rate of product? Math never lies; crunch the numbers, and you will find inefficiencies.

Overfilling mixers has been a common problem associated with manufacturing low-density diets when consistent batch sizes (based on pounds) are maintained. The inclusion of low-density alternative ingredients fills the mixer above the ribbon or paddle diameter; consequently, certain products (usually micro-ingredients and liquids) do not get folded into the mix and homogenized throughout the batch.

Note that mixer fill observations appear different when the mixer is operating versus when it is shut off (i.e., you may be able to see the ribbon/paddle when the mixer is off, but not while it is operating).

To regain overall efficiency, set up base batch sizes based on density. In other words, determine the volume of the mixer and the predicted density of the diet, and then adjust tons per batch accordingly.

For example, based on the data in Table 1, a "six-ton" mixer (342 cu. ft. at 35 lb./cu. ft.) may be able to effectively mix only a 5.7-ton batch of a 30% DDGS diet (33.2 lb./cu. ft.), but the same mixer may also be able to effectively mix 6.2 tons per batch of a corn/soy diet (36.1 lb./cu. ft.), which is a difference of 0.5 ton per batch.

While this may not seem significant for each batch, over a week's production (10 hours per day for five days) using a five-minute batch cycle (12 batches per hour), this will result in a theoretical loss of 300 tons per week. Maximize capacity by basing decisions on rated volume capacities rather than weight capacities.


Pelleting parameters

Pellet mill operating parameters will also need to be adjusted based on changes in diet densities. Such parameters might include the pellet mill ramp-up mode, the volumetric feeder setting, the conditioner cleanout cycle associated with a pellet mill plug and the steam control setting.

A design parameter that is often overlooked is the limited amount of volume within the pellet die chamber, as well as the open area and volume of the die.

Rollers in a pellet mill serve two functions: compression and extrusion; the lower the meal density, the greater the compression vectors required. In other words, the higher the compression:extrusion ratio, the lower the efficiency. As a result, low-density alternative ingredients result in lower throughputs and lost efficiencies (Table 2).

Set your production/conditioning parameters based on volume, and when a plug occurs, go out to the work floor and watch the system as it recovers. This will help you troubleshoot pelleting problems.


Feed delivery

The effect of density and feed form at loadout may or may not be a production issue, depending on the number of loadout bins and the design of the feed trailer or truck body.

Currently, not all feed trailer or truck bodies can accommodate 24-ton loads of low-density feed. Efficiency will be lost if the load size has to be reduced or if drivers have to shovel feed to make it fit into the compartments.



The take-home message is that the densities of ingredients and changes in diet formulations can dramatically affect storage capacity, batching and mixing times, pellet mill throughput and feed delivery.

The feed mill manager, nutritionist and purchasing agent(s) must work together to minimize the effect of density on the overall efficiency of the feed manufacturing process.

In the end, the reduction in feed costs due to ingredient savings normally exceeds the manufacturing costs. However, when the entire feed team has a better understanding of how density affects both capacity and efficiency, they can make more informed decisions on the true cost savings of low-density alternative ingredients.


1. Ingredient density comparisons









-Density, lb./cu. ft.-

Whole corn






Ground corn






Soybean meal






Source: Feed technology class lab at Kansas State University, Manhattan, Kan.


2. Production and physical characteristics of diets containing DDGS



10% DDGS

20% DDGS

30% DDGs

Production rate (tons/hour)










Mash bulk density (lb./cu. ft.)





Pellet bulk density (lb./cu. ft.)





Source: A.C. Fahrenholz, K.C. Behnke and L.J. McKinney. 2013. Processing of pelleted feeds using pelleted DDGS as an ingredient. J. App. Eng. Ag. Vol. 29(1):89-92.


Volume:86 Issue:22

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