Developing New Stored Grain Pack Factors
( A collaborative project with the University of Georgia , Kansas State University, and the University of Kentucky )

Existing packing factor data are of unknown reliability and their accuracy has been questioned by the industry. Accurate data are required for government-mandated inventory control and are a crucial component of new quality management systems being developed to enable source verification in the grain handling industry.

Project Impact

New packing factors will allow warehouse officials and stored grain managers to accurately assess and track stored-grain inventories.

What is Pack factor ?
(Pack | Packing Factor |Compaction)
It is a correction factor used to accurately
determine weight of grain stored in a grain bin

use grain volume measurements
combine with grain test weight (bulk density)
measurement
accounts for the additional compaction from

the overbearing pressure of grain above

Experimental Methods :

Pack factor data will be gathered from commercial and on-farm bins and grain compaction will be measured for numerous grain samples in the laboratory. In the field, grain inside vertical storage bins constructed of steel and concrete will be measured using a laser distance meter. The mass of grain and grain properties, such as test weight and moisture content inside individual grain bins will be obtained from the collaborators.

 On-Site Measurements :
The measuring team will measure grain volume (i.e., bin diameter, eave height, grain height and cone dimensions, hopper-bottom dimensions, and false-floor height) and needs access for these. Two visits may be necessary to also obtain the empty bin measurements.

 Data from Collaborators :
Collaborators are asked to track accurately the total scale weights (net load, in lb) and grain properties (test weight, moisture content, dockage/BCFM) in individual grain bins. In elevators, our team can help monitor the tickets as trucks arrive to obtain this information. If it is a weigh out or a transfer from pile to grain bin, we need the accurate tracking of weights and grain properties and we measure the grain bin. On farms bins, we measure the bin and usually obtain the tickets after they sell the grain.

Stored-grain packing is defined as the increase in grain bulk density due to the compressibility of grain when subjected to the cumulative weight of overlying material in a storage unit. The major variables affecting stored-grain packing are grain type, moisture content, test weight, and bin geometry and dimensions. The project outcome will be a user-friendly, windows-based software that can be used by farmers, elevator managers, and government officials. The objective of the project is to refine and validate a procedure with known accuracy, based on measurable physical parameters, for determining the packing of grain within upright storage bins. Data will be gathered from commercial and on-farm bins to validate the model. Grain inside vertical storage bins constructed of steel and concrete will be measured using a laser distance meter. The mass of grain (net load, in lb) and grain properties (test weight, moisture content, dockage/BCFM, etc.) inside individual measured grain bins will be obtained from the collaborator.

Scientific Background
Grain is somewhat compressible when subjected to the cumulative weight exerted from the overlying material in a storage unit. The degree of compression depends on a number of variables related to grain type and condition, bin materials, and the size and geometry of the storage unit. Compression causes packing, which increases the bulk density of the material and, thus, increases storage-unit capacity. Accurate packing factors are required to determine the mass of grain in storage from bin dimensions and test weights. Inventory control is critical for stored-grain managers, due to the financial aspects (auditing by state agencies, loan and insurance purposes), and for new comprehensive auditing and management systems. Several studies in the literature have attempted to develop a simple and convenient method for estimating the amount of packing in grain-storage structures (e.g., Bates, 1925 and Malm and Backer, 1985), but these have not led to a universally accepted method. A more comprehensive model for determining the packing factors for a wide range of grains and bins has been developed (Thompson et al., 1987, 1990, and 1991). While this model has not been calibrated for widespread use, it provides an excellent starting point for the experimental work. The model describes the physics of grain packing and, thus, only requires the measurement of representative levels of the variables rather than all combinations of variables. For packing-factor results from this model to be accurate and accepted nationwide, they must be calibrated over the range of planned usage.

Goals & Objectives
The objective of the project is to refine and validate a procedure with known accuracy, based on measurable physical parameters, for determining the packing of grains within upright storage structures. Factors identified for the study are: (1) structural shape and size, (2) bin wall type, (3) type of grain, (4) time in storage and impact of facility aeration systems, (5) bulk density (test weight) of the incoming grain, (6) moisture content of the grain, (7) additional factors such as broken material and fines in the grain.

Specific Objectives :
Develop new stored grain pack factors for six grains: wheat, corn, soybean, sorghum, oats, and barley
Obtain nationwide field data of pack factors in a wide range of bin sizes (small farm bin sizes up to
Million bushel bins) along with laboratory compaction data
Incorporate new pack factors into a user-friendly, windows-based software package for use by the grain
industry  

The major variables affecting stored-grain packing are grain type, moisture content, test weight, and bin geometry and dimensions. Variations in packing across different regions of the U.S. must also be investigated as well as other minor factors. In order to avoid the excessive cost of experimentally determining packing factors for all grains and conditions, we will use the preliminary, science-based model mentioned previously to reduce the total number of measurements required to achieve valid results. Physical properties will be measured in the laboratory to use as inputs for modeling. Uniaxial compression will be measured for the bulk grain using established methods and equipment in the University of Kentucky granular mechanics laboratory (McNeill et al., 2004).

The preliminary model (Thompson et al., 1987) will be calibrated in this study. This model employs the differential form of Janssen's equation to estimate the pressure and in-bin bulk density for a given depth of grain in a bin (Ross et al., 1979). We will calibrate and validate this model by measuring packing for the six grains: wheat, corn, grain sorghum, soybeans, oats, and barley. Calibrating this model instead of developing packing factors from field measurements alone will allow us to reduce the number of bins measured from tens of thousands, which would be required to cover the needed range for all variables in a strictly empirical model, to hundreds to validate the science-based model over that same range of variables. Validation data will be gathered from several hundred commercial and on-farm bins of different sizes. Storage bins constructed of steel (welded and corrugated) and concrete will be used. Data will be collected from all of the major grain-producing locations within the U.S., emphasizing the areas producing the most grain: the Midwest, the southern Mississippi River valley, the Central Plains, and the Northern Plains. Measured packing values will be compared to model results to determine bias and accuracy of the predictions. Model bias will be evaluated and corrected by comparing means and evaluating the correlation of packing factor to moisture content and test weight for each grain. Standard errors of calibration will be calculated to evaluate the accuracy of the corrected model. This accuracy and confidence-interval information alone will be a major improvement over the current methods for which the errors are not known. In addition, the new model should have better accuracy than the old methods because it accounts for the many important variables in grain and bin properties that affect the final packing but were not taken into account by the old method. The predictions of the new model will be compared to predictions from the old method using calculated standard errors and the model will be refined to reduce the overall standard error compared to the old method.

Dr. Mark Casada

Research Agricultural Engineer

USDA-ARS Center for Grain & Animal Health Research

Engineering & Wind Erosion Research Unit

Manhattan, KS

Tele: 785-776-2758 

Email: mark.casada@usda.gov

Dr. Sam McNeill

Associate Professor and Extension Engineer

Dept. of Biosystems & Agricultural Engineering

Research and Education Center

University of Kentucky

Princeton, KY

Tele: 270-365-7541, ext. 213

Email: smcneill@uky.edu

Dr. Mike Montross

Associate Professor

Dept. of Biosystems & Agricultural Engineering

University of Kentucky

Princeton, KY

Tele: 859-257-3000, ext. 106 

Email: michael.montross@uky.edu

Dr. Sidney Thompson

Professor

Dept. of Biological & Agricultural Engineering

University of Georgia

Athens, GA

Tele: 706-542-0873

Email: sidt@engr.uga.edu

Dr. Ronaldo Maghirang

Professor

Dept. of Biological & Agricultural Engineering

Kansas State University

Manhattan, KS

785-532-2908

Email: maghir@ksu.edu

Participating Farms

Participating Elevators