Prediction of Dem Parameters of Coated Fertilizer Particles Based on Ga-Bp Neural Network

Abstract

To provide an efficient and reliable calibration method with reduced time cost and increased accuracy, the angle of repose (AoR) in the simulation is batch-processed based on Python and the GA-BP neural network is used to improve the prediction accuracy of the DEM parameters of coated fertilizer particles. The single-factor test data were firstly interpolated to obtain sufficient training samples, thus avoiding the drawback that the BP network tends to fall into the local minimum during the training process. Then the GA-BP neural network was trained in combination with the orthogonal combination test, and the fitted correlation coefficients were all greater than 0.975, indicating that the algorithm has strong generalization performance and good stability. The predicted values matched the expected output values, indicating that the GA-BP neural network can accurately predict the nonlinear function output, and the network predicted output can be approximated as the actual output of the function. With the actual AoR as the output value, the simulation value of the AoR was obtained as 24.457° when the coefficient of restitution (CoR), coefficient of static friction (CoSF), and coefficient of rolling friction (CoRF) were 0.509, 0.176, and 0.0332, respectively, and the relative error with the actual value was 0.068%, indicating that the well-fitted GA-BP neural network could accurately predict the DEM parameters of fertilizer particles.