Research on GPR-MPC Intelligent Control System for Paddy Rice Drying in Cross-Flow Circulating Grain Dryer

In order to improve the control accuracy and adaptability of drying control systems in complex paddy rice drying processes, the Gaussian process regression model predictive (GPR-MPC) drying process control strategy is designed. The strategy integrates the advantages of drying mathematical models and artificial intelligence algorithms. Firstly, based on the predicted moisture content of the drying mathematical model and the moisture content detection value, the Gaussian process regression is used to establish the model of moisture content prediction error. Secondly, the GPR-MPC control system is designed and simulation experiments are conducted to verify its effectiveness. Finally, the GPR-MPC intelligent control system of a grain dryer is designed and drying experiments are conducted with the grain dryer. The GPR-MPC intelligent control system testing experiment is conducted using a 15-ton cross-flow batch type recirculating grain dryer. The experimental result shows that the maximum, average, and variance of the grain moisture content control error are 0.4%, 0.165% and 0.114%, respectively. Compared to the MPC control system, the designed GPR-MPC intelligent control system has high prediction accuracy, small moisture content control error, and stable control system operation. The integration of drying mathematical models and artificial intelligence algorithms can effectively improve the drying effect and reduce dependence on data volume. This research is of great significance for promoting the development of intelligent drying technology.