Numerical study of particle mixing in a lab-scale screw mixer using the discrete element method

Abstract

This study employs the Discrete Element Method (DEM) to simulate particulate flow and investigate mixing performance of a lab-scale double screw mixer. The simulation employs polydispersed biomass and glass bead particles based on experiments conducted in previous studies. Visual examination of particle distribution and statistical analysis of particle residence times of experimental data served as model validation. Statistical analysis indicates a maximum 9.8% difference between the experimental and simulated biomass particle mean residence time, and visual observations suggest the simulation captures the particle mixing trends observed in the experiments. Results indicate that the particle mean mixing time, non-dimensionalized by ideal flow time in the plug flow reactor, varies between 1.008 and 1.172, and it approaches 1 with increasing biomass feed rate. The mixing index profile in the axial direction shows a mixing-demixing-mixing oscillation pattern. Increasing screw pitch length is detrimental to mixing performance; decreasing the solid particle feed rate reduces the mixing degree; and increasing the biomass to glass bead size ratio decreases mixing performance. A comparison of a binary, single-sized biomass and glass particles mixture to a multicomponent mixture indicates that the binary system has similar mixing pattern as a multicomponent system. These findings demonstrate that DEM is a valuable tool for the design and simulation of double screw mixing systems.

Large-scale GPU based DEM modeling of mixing using irregularly shaped particles

“…A number of computational approaches have been developed [4,5,6, 7] to predict particle mixing that include isolated cases of continuum models [8] and multi-scale continuum models [9], while the majority of studies considered discrete element models [10,11,12,13,14,15,16,17,18,19]. DEM has been shown to be capable to predict sensitive particle scale effects in mixing [10].…”

“…DEM has been shown to be capable to predict sensitive particle scale effects in mixing [10]. Discrete element particle mixing studies have been confined to the modelling of spherical particles [10,11,12,13,14,15,16,20], although the importance of particle shape and angularity from experimental [21,22] and discrete element studies [10] have highlighted the importance thereof. Laurent and Cleary [23] showed that approximating particles as spherical in a plough mixer under-predicted the free surface angles, leading to lower shear resistance.…”

A DEM modeling of biomass fast pyrolysis in a double auger reactor

“…While there are still a lot of challenges in experimentally measuring flow information in granular flows, the installment of internal structures in the auger reactor makes it even harder to probe local granular flow information. Numerical simulation, as an alternative way, is capable of predicting granular flow and heat transfer behaviors in the reactor and providing useful information such as particle residence time, particle mixing degree, and heat transfer coefficient [10,11] for reactor diagnosis.…”

Scale-up in Turbula® mixers based on the principle of similarities

“…These complex systems cannot be easily described through a limited number of physical parameters (Ammarcha et al 2013). Simulation could be used to understand mixing mechanisms inside the blender in order to improve design or operations (Yamamoto, Ishihara, and Kano 2016) (Qi, Heindel, and Wright 2017) (Xiao et al 2017), but this data is difficult to transpose to a real material, for which shapes and sizes of particles are widely dispersed. Therefore, the optimization of mixing or a formulation step cannot be performed without experimental work that is usually tedious.…”