Predicting Mixing: A Strategy for Integrating Machine Learning and Discrete Element Method

Abstract

Segregation, the opposite of mixing, poses a common challenge in granular systems. Using a rotating drum as the basic mixing equipment, the fundamental focus of this study is to quantify undesirable segregation. The impact of particle level parameters (size, density, their combination, mass fraction) and system parameters (filling %, rotational speed, and baffle) on the segregation index within the rotating drum is first assessed using the discrete element method (DEM). Later, the machine learning (ML) model is applied in conjunction with DEM to expand and fill in the parameter space for particle-level parameters in a computationally efficient way, providing accurate predictions of segregation in less time. The DEM results are validated by comparing them with experimental data, ensuring their accuracy and reliability. The results show that optimal mixing is achieved when the total filling percent in a system is 36.3% while maintaining an equal proportion of particles. The highest level of mixing occurs at 60 rotations per minute, with fine particles concentrating near the drum’s core and coarser particles distributed around the periphery. The presence of 3–4 baffles optimally enhances mixing performance. Four ML models─linear regression, polynomial regression, support vector regression, and random forest (RF) regression─are trained using data from DEM simulations to predict the segregation index (SI). An error analysis is performed to pick the best model out of the four ML models. The analysis reveals that the RF model accurately predicts the SI. Using the RF model, the SI can be reliably predicted for any value of the seven features studied using DEM. An example 3D surface plot is generated by considering just two (out of 7) of the most important particle level parameters: size and density. The result shows that while both particle size and density contribute to segregation, variations in particle size appear to have a more pronounced effect on the SI compared to particle density.