Simulation of granular flows and machine learning in food processing

Granular materials are widely encountered in food processing, but understanding their behavior and movement mechanisms remains in the early stages of research. In this paper, we present our recent modeling and simulation work on chute granular flow using both the discrete element method (DEM) and continuum method. Based on the simulation data, we apply machine learning techniques such as Random Forest, Linear Regression, and Ridge Regression to evaluate the effectiveness of these models in predicting granular flow patterns. The granular materials in our study consist of soft-sphere particles with a 1 mm diameter, driven by gravity as they flow down a chute inclined relative to the horizontal plane. Our DEM and continuum simulation results show good agreement in modeling the chute flow, and the machine learning approach demonstrates promising potential for predicting flow patterns. The results of this chute flow study can provide a benchmark solution for more complex flow problems involving factors such as particle shape, size, interparticle interactions, and external obstacles.