Peiran Zhou
ABSTRACT
Storage is the key part of grain logistics where aeration is very essential to keep the grain safe. Exploring the intelligent method in modeling of stored grain aeration is helpful to effectively predict the aeration process and solve the problem of on-line calculation. The BP neural network was used for modeling in this paper. Firstly, the mechanical model used to collect data for modeling was presented and the topology of the neural network was determined. The initial grain moisture, initial grain temperature, air inlet air velocity, inlet air temperature, inlet air relative humidity and aeration time were chosen as the input variables, and the temperature and moisture of grain, the intergranular air temperature and humidity were the output variables. Then the BP neural network was trained and tested using the collected data. The simulation results showed that the BP neural network model could accurately predict the temperature and moisture content of grain during aeration, and provided an intelligent solution for stored grain aeration modeling.
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- Modeling of stored grain aeration based on BP neural network
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