Effect of Temperature Sensor Numbers and Placement on Aeration Cooling of a Stored Grain Mass Using a 3D Finite Element Model

Abstract

Grain stored in silos in the United States of America is generally cooled with an aeration system to limit mold spoilage and insect infestation. Monitoring efficacy of aeration and real-time conditions of stored grain is generally done using temperature cables with fixed-spaced sensor locations that are hung from the roof of the silo. Numerous placement options exist in terms of the number of cables and their positions. However, little investigation has been done into the effects of cable placement on aeration system operation decisions and real-time monitoring of stored grain conditions. For a one-year period, the temperatures predicted by sensors in three recommended temperature cable configurations were evaluated for conditions in Ames, IA, USA. The average temperatures of each of the cable sensor configurations were lower than the average temperatures of the entire silo, with as much as an 11.4 °C difference. When sensor locations were used as inputs for aeration control, all cable sensor configurations predicted similar average temperatures. However, the temperature averages varied by as much as 3.6 °C depending on the temperature cable distribution chosen. Results demonstrated that temperature cables near the center or near the edges of the silos produce results that are not representative of the grain mass, resulting in less efficient aerations. Simulations were also conducted with randomized horizontal “wireless” sensor locations at fixed grain depths. The average temperatures were similar, but an increase in the number of sensors reduced variability between simulated storage years as the number of randomized sensors increased.

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“…Results predicted by a 3D finite element computational model demonstrated that temperature cables in the center or near the edges of the silos were not representative of average temperatures in the grain mass, resulting in too infrequent or excessive aeration, respectively. Placement of "wireless" sensors at fixed grain depths but randomized horizontally along the diameter resulted in similar average temperatures, while an increase in randomized sensor numbers reduced variability among years of weather data simulated [7]-Consortium; (8) Use of near infrared hyperspectral imaging to evaluate color, firmness, and soluble solid content (SSC) of Korla fragrant pears. This study acquired hyperspectral imaging data for 200 samples to construct statistical evaluation models for predicting these quality parameters using iteratively retaining informative variables (IRIV) and least square support vector machine (LS-SVM) analysis.…”