Segregation of powder mixtures at filling and complete discharge of silos

Engblom, Niklas; Saxén, Henrik; Zevenhoven, Ron; Nylander, H; Enstad, Gisle G.

doi:10.1016/j.powtec.2011.09.033

Cited by 25 publications

(8 citation statements)

References 31 publications

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“…Here, x fines expresses the mass fraction less than 125 μm, d 50 is the mean particle size for the size fraction greater than (subscript "coarse") or less than (subscript "fines") 125 μm, and ρ s is the particle solid density for the size fraction greater than (coarse) or less than (fines) 125 μm. Segregation at discharge was shown to be mainly determined by the material distribution resulting from filling, but a regression model for segregation at discharge could not be based on eq 1 in previous work 18 because of the limited number of experiments or data points available for silo emptying. This is not the case here as 110 experimental values for segregation at discharge are included in the data set.…”

Section: Segregation Datamentioning

confidence: 99%

Analysis of Segregation Data for a Dry Mineral-Based Construction Materials Plant

Engblom

Saxén

Zevenhoven

et al. 2012

Ind. Eng. Chem. Res.

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In this work, segregation data for seven commercial products at a dry mineral-based construction materials plant have been analyzed. The segregation manifests itself as an increase of the fines content to unacceptable levels at the end of complete emptying from a surge silo situated upstream of packing. Results for preliminary tests for sifting segregation and fluidization show the difficulty in estimating a priori the segregation patterns observed in practice. Knowledge of the segregation mechanisms occurring with similar materials at silo filling and the discharge flow pattern is utilized to explain the segregation at silo emptying: Accumulation of fine particles at the silo walls during filling and discharge in expanded flow cause fines-rich regions from the vicinity of walls in the lower parts of the silo to be withdrawn last. Large variations in the magnitude of segregation for each product cannot be explained for example by production run size, but are caused by size variations in the silo input, uneven distribution of fines over the heap surface during filling, unpredictable stagnant zones during discharge, and sampling procedure. Segregation at discharge mainly increases with increasing particle size distribution, but the product with the widest size distribution does not segregate the most. The width of the particle size distribution for the products correlates with the mass fraction of fine particles, and inclusion of a special additive (synthetic fibers) in very small proportions is also seen to affect segregation. Therefore, a regression model based on the mass fraction of fines and fibers was employed to organize the products according to the magnitude of segregation. Results for the model show that segregation does not always increase with an increase in the width of the particle size distribution and is negatively affected by inclusion of light and elongated particles (fibers).

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Section: Segregation Datamentioning

confidence: 99%

Analysis of Segregation Data for a Dry Mineral-Based Construction Materials Plant

Engblom

Saxén

Zevenhoven

et al. 2012

Ind. Eng. Chem. Res.

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“…[6] outside the Theory of Sampling. Numerous studies have investigated the mixing and segregation behaviour of particulate materials due to the differences in particle size, density, and shape [7][8][9][10][11]. Factors responsible for segregation during loading of hoppers include particle velocities, trajectories, the effect of friction coefficients and mixing effects [6].…”

Section: Introductionmentioning

confidence: 99%

The Grouping and Segregation Error in the Rice Experiment

Minnitt

2022

Minerals

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The grouping and segregation error, one of the seven sampling errors defined by Pierre Gy, is related to the combined effects of gravity and characteristics of the target analyte such as particle size, density, shape, and moisture content of the particulate materials being sampled. Kinetic energy acting on particulate materials that are moved, flow, transported, or stockpiled causes the spatial distribution of fragments relative to one another to change. The grouping and segregation error is identified, quantified, and measured in relative sampling variance terms by comparing the sampling variability due to fractional shovelling (scooping) with that using a Jones riffle splitter. The relative sampling variance of low concentrations, approximately 0.01%, of steel balls, lead balls, and flakes of tungsten carbide in the host substrate indicates that, in this specific sampling space, the grouping and segregation error is primarily a function of particle density. Conclusions from the experiments are that components of the grouping and segregation error, namely the grouping factor and segregation factor, can be identified, measured, and mitigated. Whereas the grouping and segregation error has historically been considered to be less than the fundamental sampling error, these experiments suggest that it can be up to four times the fundamental sampling error depending on the density of the segregated materials.

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“…The structure of a hopper is simple, but the flow behavior of the particles inside is very complicated, which is very interesting. In the past few decades, segregation of particles in hoppers has attracted great attention from scholars. , Scholars have done a lot of research work on particle size segregation in hoppers by experiments and the discrete element method (DEM). − Geometric parameters, − particle properties, − and interaction parameters have significant effects on segregation, and there is obvious percolation on the free surface . Among them, DEM, as an effective method, has been widely used in the simulation of particle motion. − Some information that cannot be directly obtained in experiment can be obtained through DEM simulations.…”

Section: Introductionmentioning

confidence: 99%

Local Percolation of a Binary Particle Mixture in a Rectangular Hopper with Inclined Bottom during Discharging

et al. 2020

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To reveal the local percolation characteristics of a binary particle mixture in a rectangular hopper with inclined bottom, the discrete element method (DEM) is used to simulate the discharging process. A local percolation evaluation method is proposed, and the percolation strength grid maps are drawn. The effects of geometric parameters, particle properties, and interaction parameters on percolation are investigated. Apart from the free surface, percolation is mainly concentrated near the wall and at the bottom. With the increase in the orifice width, the average local percolation strength index (ALPSI) of the near-wall region increases and that of the bottom region decreases. The effect of the angle on percolation in the near-wall region can be ignored. The effect of friction on local percolation is significant. Increasing the fine particle mass fraction and reducing the difference in particle size can effectively avoid percolation.

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Segregation of powder mixtures at filling and complete discharge of silos

Cited by 25 publications

References 31 publications

Analysis of Segregation Data for a Dry Mineral-Based Construction Materials Plant

Analysis of Segregation Data for a Dry Mineral-Based Construction Materials Plant

The Grouping and Segregation Error in the Rice Experiment

Local Percolation of a Binary Particle Mixture in a Rectangular Hopper with Inclined Bottom during Discharging

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