Smart Control Research In Paddy Drying Based On BP Neural Network

China has a large need for food grain, the security storage for grain is related to our nation's future and destiny. Grain must be dried to safe water content for long-term storage, but automation control is not really applied to machinery for grain drying. Purpose of this study is to improve the control level of automation for continuous flow grain dryers and moisture consistency of dried grain, as well as increasing the competitiveness of grain dryers in the international market.Taking the real continuous flow paddy dryer as a research object, many measured data are collected. Based on a physical model, a mathematical model is made to compute the paddy moisture during the drying process and then a BP neural network model is built for the control simulation of the drying process.On the basis of measured data and references and directed by the thin layer drying theory, through comparing between computed results and the measured results, some parameters in mathematical formulas are adjusted, then a mathematical model is made for continuous computation of the paddy drying process. According to simulation computation of this model, influences of many parameters including environment temperature, environment relative humidity, high and low temperature of drying medium, wind capacity, initial paddy moisture and paddy discharging speed (drying time) are discussed. A BP neural network model is built for computation of wind capacity in the control simulation of the drying process. That is, through the adjustment of amount of drying air to control the drying process as well as the paddy moisture and decrease the gap of the final moisture content of dried paddy.With the assistance of computer and MATLAB, a simulation computation of the mathematical model and a control simulation of the BP neural network model are made. Conclusions are drawn that the mathematical simulation result for the final moisture content of paddy is well in line with the measured data and the predicted values of BP neural network model meets requirements of the drying process control. Finally, a program is designed to calculate the control process of BP neural network in paddy dyring.