Modeling of Grain Storage Ventilation Based on BP Neural Network

HONG-YU LI, ZI-ZHAO JIAO, JING-YUN LIU

Abstract


In the process of grain storage, low temperature and drying are two indispensable conditions. Among them, drying is to keep safe water content of grain so as to avoid mildew and prolong its storage period. As far as drying is concerned, ventilation is the key. According to the characteristics of grain storage ventilation, combined with the heat and mass transfer theoretical model of grain storage aeration, simulation was carried out in MATLAB to obtain the corresponding ventilation duration, and neural network training and test data were obtained. Then the BP neural network model of real-time grain temperature and moisture, air temperature and humidity changes in the granary is established. MATLAB is used for simulation. The results show that the model can accurately simulate the temperature and humidity of the air and the temperature and moisture of grain after ventilation, providing a solution to grain storage ventilation modeling.

Keywords


Grain storage, Stored grain aeration, BP neural network, MATLAB, Simulation.Text


DOI
10.12783/dtcse/cmso2019/33585

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