PARROT: A Pilot Study on the Open Access Provision of Particle-Discrete Tomographic Datasets

Ditscherlein, Ralf; Furat, Orkun; Mehnert, Raik; Trunk, Robin; Leißner, Thomas; Krause, Mathias J.; Schmidt, Volker; Peuker, Urs A.

doi:10.1017/s143192762101391x

Cited by 10 publications

(4 citation statements)

References 61 publications

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“…Since the novel model also calculates and displays 3D separation efficiency data (see Figure 9), optimization with respect to one or more particle features is possible. Lastly, the feed material can be easily modified, either based on real-world or computer-generated particles derived from simulated (see Section 3.4 and Figure 4) or real-world discrete particle databases [98].…”

Section: Tcm Results For 3d Fractionationmentioning

confidence: 99%

“…For example, methods such as transmission (TEM) [94,95] or scanning electron microscopy (SEM) [96,97] are able to statistically characterize complex particle systems on a broad size range. In case of 3D image processing, Ditscherlein et al [98] introduced an open access archive for particle-discrete CT. One major advantage of module M3 in the presented workflow is that it can process such database entries independently of the measurement methodology used. This means that, in future research, real-world particle data can be easily translated into a 3D-PTD and implemented into a 3D-TCM.…”

Section: Virtual Particle System Creation and Characterizationmentioning

confidence: 99%

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Real-Time Modeling of Volume and Form Dependent Nanoparticle Fractionation in Tubular Centrifuges

et al. 2022

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A dynamic process model for the simulation of nanoparticle fractionation in tubular centrifuges is presented. Established state-of-the-art methods are further developed to incorporate multi-dimensional particle properties (traits). The separation outcome is quantified based on a discrete distribution of particle volume, elongation and flatness. The simulation algorithm solves a mass balance between interconnected compartments which represent the separation zone. Grade efficiencies are calculated by a short-cut model involving material functions and higher dimensional particle trait distributions. For the one dimensional classification of fumed silica nanoparticles, the numerical solution is validated experimentally. A creation and characterization of a virtual particle system provides an additional three dimensional input dataset. Following a three dimensional fractionation case study, the tubular centrifuge model underlines the fact that a precise fractionation according to particle form is extremely difficult. In light of this, the paper discusses particle elongation and flatness as impacting traits during fractionation in tubular centrifuges. Furthermore, communications on separation performance and outcome are possible and facilitated by the three dimensional visualization of grade efficiency data. Future research in nanoparticle characterization will further enhance the models use in real-time separation process simulation.

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Section: Tcm Results For 3d Fractionationmentioning

confidence: 99%

Section: Virtual Particle System Creation and Characterizationmentioning

confidence: 99%

Real-Time Modeling of Volume and Form Dependent Nanoparticle Fractionation in Tubular Centrifuges

et al. 2022

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“…The workflow for imaging a sample of interest requires expert knowledge and industrial grade X-ray scanners. Several online repositories have emerged recently by providing curation and hosting services to enable the wider applicability of these technologies 29 , 33 . Namely, DRP provides open-access to samples from various origins (such as those in Fig.…”

Section: Methodsmentioning

confidence: 99%

“…In order to push the state of the art for ML forward and provide comparisons between ML techniques, it would be very useful to have a large number of labeled data (the results of expensive full-physics simulations) to build models that work in the complexity of real-world pore structures. For porous media, datasets of natural examples are beginning to be assembled, but of limited scale 28 , 29 and lack flow information. Large, labeled datasets are usually limited to synthetic examples 27 and/or 2D flow.…”

Section: Background and Summarymentioning

confidence: 99%

A Dataset of 3D Structural and Simulated Transport Properties of Complex Porous Media

et al. 2022

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Physical processes that occur within porous materials have wide-ranging applications including - but not limited to - carbon sequestration, battery technology, membranes, oil and gas, geothermal energy, nuclear waste disposal, water resource management. The equations that describe these physical processes have been studied extensively; however, approximating them numerically requires immense computational resources due to the complex behavior that arises from the geometrically-intricate solid boundary conditions in porous materials. Here, we introduce a new dataset of unprecedented scale and breadth, DRP-372: a catalog of 3D geometries, simulation results, and structural properties of samples hosted on the Digital Rocks Portal. The dataset includes 1736 flow and electrical simulation results on 217 samples, which required more than 500 core years of computation. This data can be used for many purposes, such as constructing empirical models, validating new simulation codes, and developing machine learning algorithms that closely match the extensive purely-physical simulation. This article offers a detailed description of the contents of the dataset including the data collection, simulation schemes, and data validation.

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Solid–Solid Separation

Peuker,

Leißner

2023

Ullmann's Encyclopedia of Industrial Chemistry

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The article contains sections titled: 1 Introduction 2 Fields of Application 2.1 Primary Raw Materials (Minerals) 2.2 Secondary Raw Materials (Recycling Products) 2.3 Construction Materials 2.4 Agricultural, Biotechnological, and Food Raw Materials 2.5 Chemical Production 3 Schematic Description of Solid–Solid Separation 3.1 Preconditions 3.2 Liberation 4 Quantification of a Solid–Solid Separation Process Result 5 Presentation of the Result of a Solid–Solid Separation Process 5.1 Grade‐Recovery Curve to Quantify Solid–Solid Separation 5.2 Tromp Curve to Quantify Solid–Solid Separation 5.3 Multidimensional Quantification of Solid–Solid Separation 6 Separation Principles for Solid–Solid Separation 6.1 Manual and Automated Sorting 6.2 Density Separation 6.3 Magnetic Separation 6.4 Separation in Electric Fields 6.5 Flotation 7 Economic Aspects References

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PARROT: A Pilot Study on the Open Access Provision of Particle-Discrete Tomographic Datasets

Cited by 10 publications

References 61 publications

Real-Time Modeling of Volume and Form Dependent Nanoparticle Fractionation in Tubular Centrifuges

Real-Time Modeling of Volume and Form Dependent Nanoparticle Fractionation in Tubular Centrifuges

A Dataset of 3D Structural and Simulated Transport Properties of Complex Porous Media

Solid–Solid Separation

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