Two-dimensional particle shape analysis from chord measurements to increase accuracy of particle shape determination

Petrak, D.; Dietrich, Stefan; Eckardt, G.; Köhler, Markus

doi:10.1016/j.powtec.2015.06.036

Cited by 21 publications

(17 citation statements)

References 8 publications

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“…Although the application of µ-CT in the geosciences was previously introduced by Ketcham and Carlson (2001) and Ketcham (2005), many investigations that analyze irregular particle shapes are still performed with 2-D approaches (Buller et al, 1990;Zhang et al, 2016;Petrak et al, 2015). So far, µ-CT studies of 3-D features have mostly been related to the characterization of volcanic rocks (Eiríksson et al, 1994;Riley et al, 2003;Shea et al, 2010;Vonlanthen et al, 2015) or spheroidal objects (Robin and Charles, 2015), and these features are usually described by means of equivalent size or shape parameters, such as roundness or aspect ratio (Little et al, 2015;Saraji and Piri, 2015).…”

mentioning

confidence: 99%

Classification and quantification of pore shapes in sandstone reservoir rocks with 3-D X-ray micro-computed tomography

et al. 2016

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Abstract. Recent years have seen a growing interest in the characterization of the pore morphologies of reservoir rocks and how the spatial organization of pore traits affects the macro behavior of rock-fluid systems. With the availability of 3-D high-resolution imaging, such as x-ray microcomputed tomography (µ-CT), the detailed quantification of particle shapes has been facilitated by progress in computer science. Here, we show how the shapes of irregular rock particles (pores) can be classified and quantified based on binary 3-D images. The methodology requires the measurement of basic 3-D particle descriptors (length, width, and thickness) and a shape classification that involves the similarity of artificial objects, which is based on main pore network detachments and 3-D sample sizes. Two main pore components were identified from the analyzed volumes: pore networks and residual pore ganglia. A watershed algorithm was applied to preserve the pore morphology after separating the main pore networks, which is essential for the pore shape characterization. The results were validated for three sandstones (S 1 , S 2 , and S 3 ) from distinct reservoirs, and most of the pore shapes were found to be plate-and cube-like, ranging from 39.49 to 50.94 % and from 58.80 to 45.18 % when the Feret caliper descriptor was investigated in a 1000 3 voxel volume. Furthermore, this study generalizes a practical way to correlate specific particle shapes, such as rods, blades, cuboids, plates, and cubes to characterize asymmetric particles of any material type with 3-D image analysis.

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confidence: 99%

Classification and quantification of pore shapes in sandstone reservoir rocks with 3-D X-ray micro-computed tomography

et al. 2016

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“…In practice, both continuous distributions in Equation are approximated as piecewise discrete distributions (bins).Then assuming that the kernel is constant in the bin size, Equation 13 in discrete form becomes

q^{mea} = A y^{id}

where q mea is the vector of CLD % in each bin, A is a transition matrix and y id is the vector of bin %s predicted by the idealized geometric model. The solution of Equation to predict the ideal PSD (

y_{i}^{id}

) from a geometric model has been the main approach used in the literature over the last 18 years, including recently proposed methodologies . In this work, we tested the applicability of this approach for the type of crystals and concentrations being studied.…”

Section: Model Evaluationmentioning

confidence: 99%

Determining particle‐size distributions from chord length measurements for different particle morphologies

Schoell¹,

Irizarry

Sirota

et al. 2019

AIChE Journal

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A recently proposed model to determine particle‐size distributions (PSDs) from chord length measurements has been applied to different particle morphologies, namely compact, platelet‐ and rod‐shaped particles. To study these systems, chord length distributions (CLDs) were measured at varying particle size and solids concentration for each compound and were subsequently utilized to determine the system‐specific parameters. Each model was successfully applied to its respective compound such that the experimental PSDs and model predictions were in good agreement. Moreover, the effect of other variables such as agitation rate and solvent composition was investigated and found to be negligible for the specific systems tested. Finally, potential model optimizations of the general model construct have been studied. Two variants of the CLD compression step, namely principal component analysis and a geometric model have been considered as surrogate models. However, neither of these approaches yielded superior results than the previously proposed approach.

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“…With another fiber optic, a pulse signal of the passing particle is detected, whereby the pulse length can be related to the chord length x CL of the particle with the measured particle velocity . The advanced spatial filtering velocimetry is commercially available by the Parsum GmbH as inline probe system with a particle sizing range from 50 to 6000 µm . Since the advanced spatial filtering velocimetry presupposes single particles in the measurement zone, the particles must be diluted for an inline monitoring of dense particle systems .…”

Section: Introductionmentioning

confidence: 99%

“…The advanced spatial filtering velocimetry is commercially available by the Parsum GmbH as inline probe system with a particle sizing range from 50 to 6000 µm . Since the advanced spatial filtering velocimetry presupposes single particles in the measurement zone, the particles must be diluted for an inline monitoring of dense particle systems . For this purpose, the Parsum GmbH developed a particle dispenser unit, which makes it suitable for particle concentrations up to 30% by volume for particles >1 mm and up to 12% by volume for particles <1 mm .…”

Section: Introductionmentioning

confidence: 99%

Investigations on the Capability of the Statistical Extinction Method for the Determination of Mean Particle Sizes in Concentrated Particle Systems

Schwarz

Ripperger

Antonyuk

2018

Part & Part Syst Charact

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Due to the fast growing number of processes involving particulate systems, simple and robust measurement techniques which enable an inline monitoring of the particle size and their concentration are urgently required, since this ensures control but also process optimization. In this work, an inline measurement technique based on the statistical extinction method is developed that provides a process monitoring for a wide range of particulate processes, such as dispersion processes and spray processes. The method allows the determination of the mean size and concentration for particle systems with the size larger than 1 µm. For this purpose, a light beam illuminates the particle system, whereby the fluctuating light intensity due to the particle movement through the light beam is detected. The statistical fluctuation of the signal can be related to a mean particle size and a particle concentration. Since concentrated particle systems cause effects that additionally influence the signal, such as multiple scattering, approaches are needed to reduce or eliminate these effects. In this work, an approach using a spatial frequency filter is applied. The experimental investigations reveal that the effects can be significantly reduced with the spatial frequency filter.

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Two-dimensional particle shape analysis from chord measurements to increase accuracy of particle shape determination

Cited by 21 publications

References 8 publications

Classification and quantification of pore shapes in sandstone reservoir rocks with 3-D X-ray micro-computed tomography

Classification and quantification of pore shapes in sandstone reservoir rocks with 3-D X-ray micro-computed tomography

Determining particle‐size distributions from chord length measurements for different particle morphologies

Investigations on the Capability of the Statistical Extinction Method for the Determination of Mean Particle Sizes in Concentrated Particle Systems

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