scholarly journals Statistical Analysis of Pore - Size Distribution Data For Tight Shales From the Scotian Shelf

1992 ◽  
Author(s):  
T J Katsube
Keyword(s):  
Statistical Analysis ◽  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Distribution Data ◽  
Scotian Shelf

scholarly journals A simple approach for predicting soil water characteristic curve of clayey soils using pore size distribution data

2021 ◽  
Vol 337 ◽  
pp. 02012
Author(s):  
Wei Yan ◽  
Emanuel Birle ◽  
Roberto Cudmani
Keyword(s):  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Soil Water ◽  
Characteristic Curve ◽  
Simple Approach ◽  
Distribution Data ◽  
Clayey Soils ◽  
Soil Water Characteristic Curve ◽  
Good Consistency

The soil water characteristic curve (SWCC) of soils can be derived from the measured pore size distribution (PSD) data by applying capillary models. This method is limited for clayey soils due to the PSD changes during SWCC testing. In this study, a suction-dependent multimodal PSD model based on probability theory is developed and used to derive SWCC. The model is validated by simulating the drying branches of SWCCs of four compacted Lias Clay samples with different initial states. A good consistency between the measured and predicted SWCC is shown.

A NEW METHOD FOR CALCULATING FRACTAL DIMENSIONS OF POROUS MEDIA BASED ON PORE SIZE DISTRIBUTION

Fractals ◽  
2018 ◽  
Vol 26 (01) ◽  
pp. 1850006 ◽  
Author(s):  
YUXUAN XIA ◽  
JIANCHAO CAI ◽  
WEI WEI ◽  
XIANGYUN HU ◽  
XIN WANG ◽  
...  
Keyword(s):  
Porous Media ◽  
Fractal Dimension ◽  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Pore Space ◽  
Fractal Theory ◽  
Fractal Dimensions ◽  
New Method ◽  
Distribution Data

Fractal theory has been widely used in petrophysical properties of porous rocks over several decades and determination of fractal dimensions is always the focus of researches and applications by means of fractal-based methods. In this work, a new method for calculating pore space fractal dimension and tortuosity fractal dimension of porous media is derived based on fractal capillary model assumption. The presented work establishes relationship between fractal dimensions and pore size distribution, which can be directly used to calculate the fractal dimensions. The published pore size distribution data for eight sandstone samples are used to calculate the fractal dimensions and simultaneously compared with prediction results from analytical expression. In addition, the proposed fractal dimension method is also tested through Micro-CT images of three sandstone cores, and are compared with fractal dimensions by box-counting algorithm. The test results also prove a self-similar fractal range in sandstone when excluding smaller pores.

pyGAPS: A Python-Based Framework for Adsorption Isotherm Processing and Material Characterisation

2019 ◽  
Author(s):  
Paul Iacomi ◽  
Philip L. Llewellyn
Keyword(s):  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Surface Area ◽  
Isosteric Heat ◽  
Material Characterisation ◽  
Mixture Adsorption ◽  
Surface Energetics ◽  
Adsorption Processing ◽  
User Friendly

Material characterisation through adsorption is a widely-used laboratory technique. The isotherms obtained through volumetric or gravimetric experiments impart insight through their features but can also be analysed to determine material characteristics such as specific surface area, pore size distribution, surface energetics, or used for predicting mixture adsorption. The pyGAPS (python General Adsorption Processing Suite) framework was developed to address the need for high-throughput processing of such adsorption data, independent of the origin, while also being capable of presenting individual results in a user-friendly manner. It contains many common characterisation methods such as: BET and Langmuir surface area, t and α plots, pore size distribution calculations (BJH, Dollimore-Heal, Horvath-Kawazoe, DFT/NLDFT kernel fitting), isosteric heat calculations, IAST calculations, isotherm modelling and more, as well as the ability to import and store data from Excel, CSV, JSON and sqlite databases. In this work, a description of the capabilities of pyGAPS is presented. The code is then be used in two case studies: a routine characterisation of a UiO-66(Zr) sample and in the processing of an adsorption dataset of a commercial carbon (Takeda 5A) for applications in gas separation.

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