Neural network-based approach for predicting the flow material in transport systems

Abstract

This article addresses the problem of effectively training a neural network to predict parameters of the output flow of a conveyor system. It discusses the problems of obtaining a complete set of data for complex branched structures of multi-section conveyor systems with different section lengths. The problem of generating a data set for training a neural network is solved using an analytical model of a transport system. The input parameters for the model include approximations of the incoming material flow and conveyor belt speed allowing to consider the oscillatory behavior of the transport system's parameters. The study also examines the impact of peak loads on the material flow at the system's entry point. The findings demonstrate that the predictive model enables effective analysis of dynamic changes in the transport system's parameters, including peak flow values.