Predictive modeling for cable free grain storage management
Date
2023-05
Authors
Wright, Jordan Jeffrey Jie-Fei
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Maier, Dirk E
Brumm, Thomas
Rosentrater, Kurt
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Abstract
The objective of this research was to develop, validate and apply a two-dimensional transient heat, mass, and momentum transfer model for predicting temperature and moisture content in stored bulk grain for cable-free management during aeration and non-aeration periods. The model predicted results that were in close agreement with experimental results, especially in the core of the grain mass, generally within 1-3 °C during aeration and 2-4 °C during non-aeration periods. Computational Fluid Dynamics (CFD) was used to study the effects of non-uniform grain depth and spreading of broken corn (BC) and fines on aeration uniformity. It was found that a peaked grain mass and a high concentration of BC and fines in the core of a grain mass increased non-uniformity of airflow substantially. As a consequence, needed fan run hours and associated electric energy consumption and costs to complete an aeration cooling, warming, drying or conditioning objective can double because of the slower moving front through the core versus periphery of a grain mass. For example, cooling and drying times increased by as much as 75 and 300 hours, respectively, when aerating a peaked grain mass with a high concentration of BC and fines compared to a leveled grain mass with a more spread concentration of BC and fines. The model was also used to evaluate the effects of a temperature-based versus EMC-based fan controller on cooling and warming of a grain mass. In both cases, the EMC-based controller achieved the aeration objective in fewer hours and with less drastic change of moisture content of the bottom grain layer (1 m). During the cooling simulation, the bottom layer of the temperature-based controller rewetted by 1.9 percentage points while the EMC-based controller maintained moisture content at 14.9%. The model has been incorporated into a commercially available cable-free stored grain monitoring and management system and will also be available as a tool for teaching university students and training stored grain managers.
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