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United States Department of Agriculture

Agricultural Research Service

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2011 Annual Report

1a. Objectives (from AD-416)
Existing packing factor data are of unknown reliability and are widely mistrusted in 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. The current Farm Bill requires the Risk Management Agency (RMA) to determine the efficacy and accuracy of current pack factors and, as a result, they desire ARS to evaluate their existing packing factor data. The new data and model developed in this research will improve the scientific basis for predicting pack factor in stored grain. We will define, for the first time, uncertainty in predicted pack factors from the old method as well as from the new model. We will produce a user-friendly, windows-based software that can be used by farmers, elevator managers, and government officials. The software will allow the user to enter needed measurements and materials for the bin and quality factors for the stored grain. This tool will calculate the average pack factor for the bin and will provide accurate estimates of the confidence intervals for those pack factors. 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 5. the impact of facility aeration systems 6. bulk density (test weight) of the incoming grain 7. moisture content of the grain 8. additional factors such as broken material and fines in the grain

1b. Approach (from AD-416)
The major variables affecting stored grain packing are grain type, moisture content, test weight, and bin geometry and dimensions. Variation across different regions of the U.S. must also be investigated as well as other minor factors. In order to avoid the excessive cost from experimentally determining pack factors for all grains under all conditions, we plan to use science-based modeling to reduce the total amount of data required to achieve valid results. Physical properties will be measured in the laboratory to use as inputs for modeling. A preliminary model for determining pack factor for a wide range of grains and bins has been developed and is currently being calibrated in limited experiments. We will calibrate and validate this model by measuring pack for selected grains in bins spread over the major grain producing regions of the U.S. Calibrating the model instead of developing pack factors from field measurements alone will allow us to reduce the number of bins measured from tens of thousands to several hundred. Confidence intervals will be established from the field measurements and used to characterize the predictions of the new model and will be compared to confidence intervals determined for the old method.

3. Progress Report
Field Pack Factor Data - Seventeen farm bins from four locations were measured near Owensboro and Elizabethtown, Kentucky. Bins contained shelled white corn and were not leveled bins for measuring. Three groups of personnel measured several of the bins to allow a comparison amongst operators. However, we are still waiting for the ticket sale data from farm bins to obtain data on test weight and net load. Bin diameters ranged from 6 to 24 m (20 to 78 ft) and eave heights ranged from 6 to 21 m (21 to 70 ft). A new versatile device was developed for measuring grain bin volumes with both horizontal and vertical angles without interfering with the laser meter operation. Stored-grain packing factors were measured in more than 40 bins in Kansas, Oklahoma, and Iowa covering a wide range of bin sizes. Bins of concrete construction ranged up to 140-ft deep and those of corrugated steel construction ranged up to 105-ft diameter with 90-ft eave height. The majority of the packing factors measured so far were for HRW wheat and have varied from about 3% in small bins up to about 8% in large bins. The data is being used to calibrate a science-based computer model that is expected to predict packing factors with better accuracy than that achieved by the current method. Laboratory Compressibility Data - Prior to running compressibility tests, three laboratory compressibility testers were evaluated in preliminary tests with soft red winter (SRW) wheat and with pellets. The SRW wheat samples (10.5% MC and 60.5 lb/bushel) were tested using pressure ranges from 0.9 to 19.9 psi. There were differences with different treatments, especially for box 1. Other than box 1, the differences were small and represented the expected variation for these treatments. The increase in pressure (from 0.9 to 19.9 psi) increased the packing factor from approximately 2.5% to 8% for the current tests, with the exception of Box 1. Based on these results Box 1 was removed from the experiments and all subsequent tests will be conducted with the other two boxes, which displayed consistent results for different operators and different test dates. Software Development - A new user friendly, software version has been developed using the current data. This version runs in MS Excel 2007 for data input and display of results. New, clear diagrams have been added to make the software more accessible by the grain industry. This improvement in the software interface is a major step forward from the previous MS DOS version. The new version embedded inside an MS excel 2007 package, is designed with three separate tabs for “Information” (describes the software and packing theory), “Input values” (asks user for input material and bin properties interactive diagrams), and “Output values” (provides the calculated values like % packing, volume of the grain, etc., along with re-displaying the input values). This new platform is easily modified to include new additions and further improvements of input and output.

4. Accomplishments

Last Modified: 10/18/2017
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