DEM–PBM coupling method for the layering granulation of iron ore

Baba, Tomoya; Nakamura, Hideya; Takimoto, Hiroharu; Ohsaki, Shuji; Watano, Satoru; Takehara, Kenta; Higuchi, Takahide; Hirosawa, Toshiyuki; Yamamoto, Tetsuya

doi:10.1016/j.powtec.2020.09.059

Cited by 17 publications

(7 citation statements)

References 43 publications

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“…The quantity of the original powder charged into the vessel was set as 12 vol% of the vessel volume. 26) The loading mass of the original powder was 65.0 to 90.0 g. The rotational speed was set as 45 rpm to ensure good mixing of the powder. Wet granulation was performed as follows: First, a predetermined quantity of water was sprayed uniformly onto the original powder using a two-fluid spray nozzle.…”

Section: Wet Granulationmentioning

confidence: 99%

Determining Optimum Water Content for Iron Ore Granulation using Agitation Torque of Wet Ore Powder

et al. 2022

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Wet granulation of iron ore powders is a key process in ironmaking. In wet granulation, it is important to determine the optimum content of water added to the original ore powders. To determine the optimum water content, it is important to understand the saturation state in wet ore powder, which can be done by measuring the agitation torque of the wet powder. This study proposes a methodology for determining the optimum water content of various iron ore powders using the agitation torque of wet ore powders. First, measurement of the agitation torque and wet granulation of various iron ore powders were conducted. By comparing the results, it was found that the optimum water content, which was defined as the minimum water content required to diminish fine particles in the original powder, corresponded to the water content exhibiting the maximum agitation torque, regardless of the original powder. Using the agitation torque at different water contents, the saturation degree S, which is the volume ratio of water to the interparticle voids, was calculated, resulting in a range of 0.999 ≤ S ≤ 1.173 at the optimum water content. This suggests that the state between the funicular and capillary states is a suitable saturation state for the wet granulation of ore powders. Consequently, it was demonstrated that it is possible to determine the optimum water content for wet granulation of various iron ore powders based on the water content exhibiting the maximum agitation torque of wet ore powders.

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Section: Wet Granulationmentioning

confidence: 99%

Determining Optimum Water Content for Iron Ore Granulation using Agitation Torque of Wet Ore Powder

et al. 2022

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“…To predict the size distribution of particles in a system, it is mandatory to model the aggregation kernel (βfalse(x,y,tfalse)) accurately. Several factorization techniques are available in the literature, where researchers have partitioned the volume and time-dependent aggregation kernel [32,33,35,36]. Sen et al [28] and Barrasso et al [30,31] have partitioned the aggregation kernel into a product of two parts: the first part is the frequency of wet position collisions between particles of volume x and y at time t (Cfalse(x,y,tfalse)), and the second part is the efficiency of those wet position collisions (ψfalse(x,y,tfalse)), i.e.…”

Section: Model Developmentmentioning

confidence: 99%

“…The authors assumed that the collision velocity between particles obeyed the lognormal distribution and used that to find a constant value of ψfalse(x,y,tfalse). In a recent study, Baba et al [33] successfully used a constant value of ψfalse(x,y,tfalse)=1×10−3 to model a rotating mill granulator containing iron ores. Since the primary goal of this work is to test the bi-directional PBM–DEM coupling framework using the developed volume- and time-dependent aggregation kernel, constant values of ψfalse(x,y,tfalse) are used throughout this study.…”

Section: Model Developmentmentioning

confidence: 99%

“…In this study, the DEM simulations are mainly used to generate the collision data among particles. In the literature, several researchers have used DEM to calculate the collision data [23,[29][30][31]33]. In these studies, researchers counted the number of particles in contact at each time step.…”

Section: (B) Discrete Element Modellingmentioning

confidence: 99%

“…Sen et al [28] and Barrasso et al [29,30] successfully implemented the PBM periodically inside DEM. Recently, Barrasso et al [22,31] and Baba et al [33] successfully used the two-way coupling model, where the authors have used the PBM more to save the computational cost and used the DEM only at some intermediate time instances. However, most of the mentioned studies did not differentiate between new and repeating collisions inside DEM.…”

Section: Introductionmentioning

confidence: 99%

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An efficient multiscale bi-directional PBM-DEM coupling framework to simulate one-dimensional aggregation mechanisms

Das

Kaur

et al. 2022

Proc. R. Soc. A.

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The mesoscale population balance modelling (PBM) technique is widely used in predicting aggregation processes. The accuracy and efficiency of PBM depend on the formulation of its kernels. A model of the volume- and time-dependent one-dimensional aggregation kernel is developed for predicting the temporal evolution of the considered particulate system. To make the developed model physically relevant, the PBM model needs three unknown parameters as input: volume-dependency in collisions, collision frequency per particle and aggregation probability. For this, the microscale discrete element model (DEM) is used. The system’s collision frequency is extracted periodically using a novel collision detection algorithm that detects and ignores duplicate collisions. Finally, a multiscale bi-directional PBM–DEM coupling framework is presented to simulate the aggregation mechanism. PBM and DEM simulations take place periodically to update the particle size distribution (PSD) and extract the collision-frequency, respectively. The coupling framework successfully explains the dependence between the PSD and the collision frequency. Additionally, computational cost of the algorithm is optimized while maintaining the accuracy of the results. Lastly, the accuracy and efficiency of the developed framework are verified using two different test cases. In one of the examples, a simple aggregation is simulated directly inside the DEM for the first time.

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