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Modeling Heat and Mass Transfer Within an Eighth-scale Grain Drying System

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  2021 ASABE Annual International Virtual Meeting  2101181.(doi:10.13031/aim.202101181)
Authors:   M. A. Lewis, S. Trabelsi
Keywords:   Drying modeling, Grain drying bin, Finite Difference, Heat transfer, Mass transfer, Microwave sensing, Real-time monitoring

Abstract. An eighth-scale grain drying system was developed with the capability of monitoring moisture content, temperature, and relative humidity at different levels within the bed of grain or seed in real-time throughout the drying process. Empirical data have been used to observe the drying front as it traverses the bed during the drying process. To further analyze the complex transport phenomenon that occurs during drying, the transfer of heat and mass were modeled. Thermodynamic and convective-heat-transfer principles were used to simulate the changes in heat and moisture throughout the bed during drying. The transfer of both entities occurs simultaneously and is influenced by parameters such as inlet air temperature, air velocity and initial grain moisture content. Values for such parameters were obtained from experiments within the laboratory. The deep bed was divided into smaller layers, and the changes in grain temperature, moisture content, humidity, and interstitial air were simulated for each layer over time using the finite difference method. Model predictions were compared to empirical data obtained from the microwave moisture sensor, 8 temperature sensors, and 4 relative humidity sensors while corn dried within the eighth-scale grain drying system. Moisture content determined using the microwave moisture sensor had a standard error of performance (SEP) ≤ 0.55% moisture content when compared to the reference oven drying method. Results from the numerical simulation compared well with the empirical data, having low error. The average error between moisture content predicted in the simulation and measured empirically was 0.42%. The maximum error observed was 0.87%.

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