Tobacco Plant Harvester

A harvester for tobacco plants is provided for towing by a prime mover to which an articulated frame is attached, the frame mounting a movable turret having spears on which tobacco stalks are impaled. During removal of the stalks from the spears, they are loaded on sticks which are then manually removed from the harvester. A hydraulic system powered from the prime mover serves to actuate each of the turrets, an empty stick supplying mechanism, a loaded stick removing mechanism, and the mechanism for transferring stalks from the spears to the sticks; and a mechanical power takeoff shaft driven by the prime mover drives the conveyor which moves stalks after being cut from their root systems to the turret for impaling on the spears

Storage of Burley Tobacco in Bales and Bundles

Bales and bundles of burley tobacco were stored for seven months from spring to fall. Leaves darkened during storage at all moisture levels and stalk positions with the exception of the bottom stalk position, which darkened only slightly. There was no difference in color change and dry weight loss between burley tobacco in bales and bundles. Normal and high moisture bales and bundles were often graded as unsound because of a deviant odor caused by bacterial activity. A bale weight loss of about 8% occurred at normal moisture with the loss being divided evenly between moisture and dry weight losse

Feed pellet and corn durability and breakage during repeated elevator handling

Pelleting of animal feeds is important for improved feeding efficiency and for convenience of handling. Pellet quality impacts the feeding benefits for the animals and pellet integrity during handling. To compare the effect of repeated handling on the quality of feed pellets and corn, a 22.6‐t (1000‐bu) lot of feed pellets made from corn meal and a 25.4‐t (1000‐bu) lot of shelled corn, were each transferred alternately between two storage bins in the USDA‐ARS, Grain Marketing and Production Research Center research elevator at Manhattan, Kansas, at an average flow rate of 59.4 t/h. Samples from a diverter‐type sampler were analyzed for particle size distribution (by sieving) and durability (by the tumbling box method). The apparent geometric mean diameter of pellet samples decreased with repeated transfers, whereas the mass of accumulated broken pellets increased with repeated transfers. The percentage of broken pellets increased by an average of 3.83% with each transfer from an initial value of 17.5%, which was significantly different from the values obtained from shelled corn (p 0.05) during the transfers. The durability index of shelled corn was also not significantly different during the transfers. Analysis of dust removed by the cyclone separators showed that the mass of dust < 0.125 mm was significantly less for feed pellets (0.337 kg/t of pellet mass) than for shelled corn (0.403 kg/t of corn mass)

Material and interaction properties of selected grains and oilseeds for modeling discrete particles

Experimental investigations of grain flow can be expensive and time consuming, but computer simulations can reduce the large effort required to evaluate the flow of grain in handling operations. Published data on material and interaction properties of selected grains and oilseeds relevant to discrete element method (DEM) modeling were reviewed. Material properties include grain kernel shape, size, and distribution; Poisson's ratio; shear modulus; and density. Interaction properties consist of coefficients of restitution, static friction, and rolling friction. Soybeans were selected as the test material for DEM simulations to validate the model fundamentals using material and interaction properties. Single‐ and multi‐sphere soybean particle shapes, comprised of one to four overlapping spheres, were compared based on DEM simulations of bulk properties (bulk density and bulk angle of repose) and computation time. A single‐sphere particle model best simulated soybean kernels in the bulk property tests. The best particle model had a particle coefficient of restitution of 0.6, particle coefficient of static friction of 0.45 for soybean‐soybean contact (0.30 for soybean‐steel interaction), particle coefficient of rolling friction of 0.05, normal particle size distribution with standard deviation factor of 0.4, and particle shear modulus of 1.04 MPa

Field-Observed Angles of Repose for Stored Grain in the United States

Citation: Bhadra et al. (2016). Field-Observed Angles of Repose for Stored Grain in the United States. Applied Engineering in Agriculture, 33(1), 131-137. Doi:10.13031/aea.11894Bulk grain angle of repose (AoR) is a key parameter for inventorying grain, predicting flow characteristics, and designing bins and grain handling systems. The AoR is defined for two cases, piling (dynamic) or emptying (static), and usually varies with grain type. The objective of this study was to measure piling angles of repose for corn, sorghum, barley, soybeans, oats, and hard red winter (HRW) wheat in steel and concrete bins in the United States. Angles were measured in 182 bins and 7 outdoor piles. The piling AoR for corn ranged from 15.7° to 30.2° (median of 20.4° and standard deviation of 3.8°). Sorghum, barley, soybeans, oats, and HRW wheat also exhibited a range of AoR with median values of 24.6°, 21.0°, 23.9°, 25.7°, and 22.2°, respectively. Angles of repose measured for the seven outdoor piles were within the ranges measured for the grain bins. No significant correlation was observed between AoR and moisture content within the narrow range of observed moisture contents, unlike previous literature based on laboratory measurement of grain samples with wider ranges of moisture content. Overall, the average measured piling AoR were lower than typical values cited in MWPS-29, but higher than some laboratory measurements

Size distribution and rate of dust generated during grain elevator handling

Dust generated during grain handling can pose a safety and health hazard and is an air pollutant. This study was conducted to characterize the particle size distribution (PSD) of dust generated during handling of wheat and shelled corn in the research elevator of the USDA Grain Marketing and Production Research Center and determine the effects of grain lot, repeated transfer, and grain types on the PSD. Dust samples were collected on glass fiber filters with high volume samplers from the lower and upper ducts upstream of the cyclone dust collectors. A laser diffraction analyzer was used to measure the PSD of the collected dust. For wheat, the size distribution of dust from the upper and lower ducts showed similar trends among grain lots but differed between the two ducts. The percentages of particulate matter (PM)‐2.5, PM‐4, and PM‐10 were 5.15%, 9.65%, and 33.6% of the total wheat dust, respectively. The total dust mass flow rate was 0.94 g/s (equivalent to 64.6 g/t of wheat handled). For shelled corn, the size distributions of the dust samples from the upper and lower ducts also showed similar trends among transfers but differed between the two ducts. The percentages of PM‐2.5, PM‐4, and PM‐10 were 7.46%, 9.99%, and 28.9% of the total shelled corn dust, respectively. The total dust mass flow rate was 2.91 g/s (equivalent to 185.1 g/t of corn handled). Overall, the corn and wheat differed significantly in the size distribution and the rate of total dust generated

Applications of discrete element method in modeling of grain postharvest operations

Grain kernels are finite and discrete materials. Although flowing grain can behave like a continuum fluid at times, the discontinuous behavior exhibited by grain kernels cannot be simulated solely with conventional continuum-based computer modeling such as finite-element or finite-difference methods. The discrete element method (DEM) is a proven numerical method that can model discrete particles like grain kernels by tracking the motion of individual particles. DEM has been used extensively in the field of rock mechanics. Its application is gaining popularity in grain postharvest operations, but it has not been applied widely. This paper reviews existing applications of DEM in grain postharvest operations. Published literature that uses DEM to simulate postharvest processing is reviewed, as are applications in handling and processing of grain such as soybean, corn, wheat, rice, rapeseed, and the grain coproduct distillers dried grains with solubles (DDGS). Simulations of grain drying that involve particles in both free-flowing and confined-flow conditions are also included. Review of existing literature indicates that DEM is a promising approach in the study of the behavior of deformable soft particulates such as grain and coproducts and it could benefit from the development of improved particle models for these complex-shaped particles

Dealing with Default Judgements.

Abstract Forthcoming

Dealing with Default Judgements.

Abstract Forthcoming

Correlating Bulk Density (With Dockage) And Test Weight (Without Dockage) For Wheat Samples

Citation: Bhadra, R., Casada, M. E., Boac, J. M., Turner, A. P., Thompson, S. A., Montross, M. D.,...McNeill, S. G. (2016) Correlating Bulk Density (With Dockage) And Test Weight (Without Dockage) For Wheat Samples. Applied Engineering in Agriculture. 32(6). 925-930. 10.13031/aea.32.11692In grain bins, the compaction of stored grain is caused by the overbearing pressure of the bulk material in the bin. To predict the amount of grain in the bin, compaction values must be determined based on the average bulk density (BD) of the stored material. However, BD is determined following the Federal Grain Inspection Service (FGIS) guidelines for measuring test weight (TW), which require that dockage be removed prior to measuring wheat TW. Thus, this creates a problem for predicting grain compaction and conducting inventory studies, because the average BD of the grain in a bin for these calculations should include dockage. Therefore, regression models between the TW without dockage and the BD with dockage were obtained based on the reported scale data during wheat harvest from three elevators located in Kansas and Oklahoma. A power model was used to predict BD with dockage when TW without dockage and dockage levels are given. Laboratory samples of HRW and SRW wheat with dockage levels ranging from 0.05% to 5% showed a second order polynomial trend when plotted against decrease in BD with dockage values compared to TW without dockage. These results will be crucial for determining grain packing inventory parameters for HRW wheat bins