Location:2013 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:
Laboratory tests were designed to evaluate the effect of dockage levels on compressibility of hard red winter (HRW) wheat samples under applied pressure. Dockage levels of 0%, 1%, and 5% were tested with these samples. Results indicated that increasing dockage increased both the compressibility and variability in the data. Test weight decreased with dockage, as expected, while lower test weights produced greater compressibility, which led to compressibility increasing with increasing dockage. This test weight effect was consistent with previous results, which showed that with soft red winter (SRW) wheat samples higher initial sample density (higher test weight) yielded lower grain compressibility and, thus, lower packing. With lower initial bulk density the interstitial air space increased, which resulted in the bulk grain being more compressible. Thus, the overall effect of higher dockage was to lower test weight, which in turn increased the compressibility. A wide range of corn samples have also been tested for compressibility as a function of pressure in the laboratory compressibility box and data are being processed. Compressibility modeling studies were conducted to determine the most appropriate models for fitting the constant-moisture pressure data from the laboratory compressibility box. In addition to the standard data normally collected from the compressibility box up to about 20 psi, data were also obtained from a modified box up to 130 psi and from a small compressibility cylinder up to about 3600 psi. The density appeared to approach an asymptote in the compressibility box at the 20 psi upper limit used in standard tests. Data at higher pressures showed this was not a true asymptote and there was an inflection point where densities began to increase at a higher rate again at higher pressures. (The inflection point was about 500 psi in the compressibility sensor, but the magnitude of pressures in that cylinder is not directly comparable to that in the standard compressibility box.) The asymptotic models showed apparent asymptotes in the standard tests as high as 70 lb/bu, while density in the small compressibility cylinder reached as high as 90 lb/bu and the theoretical density limit is 110 lb/bu based on the kernel densities. Models were selected to cover the range of pressures in grain storage bins, up to 20 psi, and model predictions would be inaccurate above those levels. Overall the modified Page and Farazdaghi-Harris models were better than the others for characterizing this density data up to pressures of 20 psi. As part of the overall project to improve predictions of stored grain packing factors, existing compaction levels are being measured in the field for numerous bin sizes and types on farms and at grain elevators around the U.S. To date more than 150 bins (including both concrete and corrugated steel) have been measured across the U.S. for determining packing factor. Measurements in the fall of 2012 concentrated on corn and soybean bins with over 60 bins containing these two crops now measured. Other crops were also measured and another 40 bins containing oats, grain sorghum, and barley have been measured to determine packing factor. Measurements of bins with hard red winter (HRW) wheat are largely complete with about 60 bins measured. HRW wheat packing was measured in corrugated steel bins with diameters ranging from 4.6 to 32 m and eave heights ranging from 3 to 27 m and in concrete bins with diameters ranging from 4.6 to 10.3 m and eave heights ranging from 26 to 42 m. The maximum and median differences between the model-predicted mass and scale-measured mass were -4.2% and -1.1%, respectively, for corrugated steel bins, -8% and -0.54% for reinforced concrete bins with hopper bottoms, and 10% and 1.6% for reinforced concrete bins with off-center, side-discharge hopper bottoms (chute-bottoms). In most cases, the model under-predicted the mass with the exception of a single set of chute-bottom bins with chute angle, 34 degrees. Comparison of the difference between predictions of the old method and the scale-measured mass had a maximum value of -22.8%, with a mean absolute difference of 5.61% for all types of bins, indicating that that the new model predicted packing factor better than the old method for HRW wheat. A Windows-based software package is being developed using our model to provide the new packing factor predictions to customers. Two significant revisions to the grain packing factor prediction software were delivered to the Risk Management Agency (RMA), one in February and one in July, 2013. Most needs of the RMA officials have been addressed and we are now working with the 17 RMA-listed crop insurance providers to refine the program for their needs. The most recent version provides both input and output options that match the current hand-methods as much as possible. Other significant additions to the new versions are: an additional input to account for "cubic feet of deductions" (for aeration ducts or other items inside the bin), capability for treating different bin cross-sections (rectangular and hexagonal are now included along with round and square), an option for handling round outdoor piles, and an option to input hopper bottom dimensions without requiring a hopper angle measurement. This extensive laboratory and field data combined with modeling work is contributing to a greater understanding stored grain packing factors and should provide greater confidence in the packing factor predictions being developed compared to the old packing factor predictions.