A network-based approach to measure granule size distribution for discrete element modeling of granulation

TL;DR

The paper addresses the challenge of measuring granule size distributions in DEM-based drum granulation, where existing granule definitions fail for dense flows. It introduces a network-science approach that treats granules as communities in a force network, with edges weighted by liquid-bridge forces and communities identified via modularity optimization. The study shows that the common component-detection method yields unrealistic granule sizes and misses process-parameter effects, while the community-detection method yields granule sizes comparable to benchmark methods and captures the influence of fill level and liquid content. This network-based approach provides a robust, parameter-sensitive tool for analyzing granulation in DEM and can improve the interpretation of granulation dynamics in industrial settings.

Abstract

Drum granulation is a size enlargement process where granular material is agitated with a liquid binder to form larger size granules. Discrete element modeling is increasingly being used to better understand and investigate the granulation process. However, unlike experiments the measurement of granule size within a DEM framework often necessitates an explicit quantitative definition of a granule and a corresponding granule identification method. In this work, we show that the existing definitions and the associated methods in literature are ineffective at identifying granules for dense flows such as during drum granulation. We propose an improved definition and granule identification method based on community-detection used in network science literature. The proposed method better identifies granules in a drum granulator as benchmarked against liquid-settling. We also vary granulation process parameters like liquid content and fill level and study their effect on the cumulative granule size distribution attained after drum granulation. We find that the existing granule-identification methods fail to reproduce the well-known effects of process parameters on the cumulative granule size distribution. The proposed method, based on community detection, reproduces the effects with better accuracy.

Figures (11)

• Figure 1: Snapshot showing granulation in a drum for fill level f=16% and the total liquid content L=12%.
• Figure 2: Snapshots of (a) initial state before and (b) final state after the sieving for drum fill fraction $f=0.16$ and liquid content $L=0.12$ during granulation.
• Figure 3: Cumulative granule size distribution measured by sieving showing the effect of (a) drum fill level (for liquid content $L=12\%$) and (b) liquid content for fill level $f=16\%$.
• Figure 4: Snapshots of (a) initial condition for liquid-settling and (b) finally separated granules for fill fraction $f=0.16$ and liquid content $L=0.12$ during granulation.
• Figure 5: Cumulative granule size distribution measured by liquid-settling showing the effect of (a) fill level for liquid content $L=12\%$ and (b) liquid content for the fill level $f=16\%$.