Date of Award
Master of Science
Agricultural and Biosystems Engineering
Industrial and Agricultural Technology
Dirk E Maier
Several methods are available for stored grain monitoring such as manual grain inspection, acoustical sensors, carbon dioxide sensors, and moisture and temperature cable sensors. All of these have constraints that prevent them from being effective stored grain monitoring technologies. The overall objective of this research was to test whether novel wireless sensor technology can overcome existing constraints and can be used for effective, reliable and automated stored grain monitoring. This objective was achieved by (1) determining the accuracy and reliability of novel wireless sensors, (2) comparing the effectiveness of the novel wireless sensor technology against a conventional cable-based sensor system, (3) determining the distribution and recovery of brick and spherical shaped wireless sensors during the filling and unloading operation of stored grain silos, and (4) determining the number of sensors needed to achieve sufficient reliability for aeration cooling during the fall harvest period and monitoring of stored grain quality during the non-aerated storage period.
Wireless temperature and relative humidity (RH) sensors were checked and calibrated against three known reference salts solutions: Magnesium Chloride (33.07% RH), Sodium Chloride (75.47% RH), and Potassium Nitrate (94.62% RH). The average values obtained for the wireless sensors at these reference RH values were 2.78 percentage points (ppts) higher, 4.72 ppts lower, and 7.60 ppts lower, respectively. Two wireless sensors each were placed in a total of 12 jars with three sets of jars each containing grain at 12%, 15%, 18%, and 21% moisture content. The sealed jars were placed in an environmental chamber at 0°C, and the chamber temperature was incrementally changed by 5°C up and down from 0°C to 30°C. The wireless sensors used in these experiments were not sufficiently accurate to measure grain moisture content as values were 1.5-4.2 ppts different from the actual values over the temperature range investigated. They were also not sufficiently reliable as standard deviations were 1.1-2.3 ppts greater than for the reference salt solutions. In comparison, a precision sensor was placed sequentially in four different jars containing grain at 12%, 15%, 18%, and 21% moisture content. The sensor was sufficiently accurate and reliable to measure grain moisture content within 1.0-1.5 ppts and with standard deviations of 0.2-1.0 ppts.
Effectiveness of 14 wireless sensors was determined against cable sensors by monitoring temperature and RH in the stored grain mass. The wireless and cable-based sensors indicated the same temperature 85% of the time, and RH (and therefore the calculated equilibrium moisture content) only 78% of the time during non-aerated storage. The results documented that the wireless and cable-based sensors indicated the same temperature and RH only 25% of the time during aerated storage. The cable-based sensors were probed and left within the grain mass for a period of six months. Grain samples were taken on a weekly basis and moisture content was determined with a calibrated GAC 2500 moisture meter. The data revealed that the cable-based sensors and the GAC 2500 moisture meter indicated the same moisture content 100% of the time.
Five of each sensor shape (brick and spherical) were placed one at a time in a grain stream flowing repeatedly at 28.6 (1062 bushels per hour, bph) and 39.6 (1500 bph) Mg per hour. The drop height was 5.30 m (17.4 feet) in the first trial and 3.94 m (12.9 feet) in the second. The results indicated that the brick shaped wireless sensors tended to settle about 1/3 of a silo diameter around the center of the peaked grain mass whereas the spherical shaped wireless sensors tended to settle about 3/4 of a silo diameter from the center of the peaked grain mass (and within ¼ diameter of the silo wall). In the second experiment, 44, 15, 20, and 25 wireless sensors were randomly placed in the grain mass to test their recapture rate during four replicated unloading trials. The results indicated that all wireless sensors were recovered resulting in a 100% recapture rate but several sensors were damaged by the intake well of the screw unloading conveyor. Key findings of this study point toward the need for a mix of both spherical and brick shaped sensors of different sizes and weights to achieve targeted placement of wireless sensors within the stored grain mass as a function of gravity filling silos of different sizes. Additionally, techniques need to be developed to prevent damage to sensors during unloading as that would otherwise result in adulterated grain.
The accuracy of a previously developed 3D ecosystem model was validated and then applied to determine the number of sensors needed to reliably monitor stored grain quality based on predicted grain temperatures in three different silo sizes. In the first study, a silo was loaded with about 229 Mg (9000 bushels) of corn and six temperature cables were placed in the grain mass. The grain was aerated continuously for a period of two weeks, and the cable sensor temperature readings were compared to the predicted temperatures. The predicted temperatures were in close agreement with the observed temperatures with the standard error of prediction ranging from 2.0 to 3.7°C. In the second study, 15 and 30 sensors were placed at manufacturer recommended depths and horizontal locations in the grain mass of three silo sizes (i.e., 11x11, 14.6x14.6 and 14.6x18.3 m diameter by eave height). The average grain temperatures predicted by the 15 and 30 sensors over a one-year period were compared to the average grain temperatures predicted by the numerical solution for the entire grain mass (1968, 3052, and 3204 mesh nodes). The number of sensors needed to monitor stored grain temperatures with sufficient accuracy in the three silo sizes evaluated heavily depended on whether the aeration control strategy achieved a sufficiently low temperature by the time the aeration fans were turned off ahead of the non-aerated storage season. For the three silo sizes, 15 or 30 sensors were sufficient to monitor grain temperatures during the aeration cooling period but for the two larger silo sizes more than 30 sensors would be needed during the storage period. As silo size increased, and surface-to-volume ratio decreased, grain temperatures remained lower during the storage period. Results support the best management practice recommendation of leaving cooled grain cold and not warming it up in the spring ahead of storage into the summer.
Guy Roger Aby
Aby, Guy Roger, "Wireless sensors for quality monitoring and management of stored grain inventories" (2020). Graduate Theses and Dissertations. 18081.