Textural characterization of active carbons using fractal analysis

Mahamud, Manuel María

doi:10.1016/j.fuproc.2006.06.006

Cited by 23 publications

(27 citation statements)

References 12 publications

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“…These methods have already been used to characterize series of materials constituted by oxidized coals [11], chars [12] and active carbons [13]. Recently, Zhang et al [31] proposed an improvement of their procedure.…”

Section: Methods Used For the Determination Of Surface Fractal Dimensmentioning

confidence: 99%

“…For actual objects the fractal character cannot be the same for the whole range of sizes. This fact opens up the possibility of obtaining fractal profiles of porous solids that constitute a very useful tool for characterizing porous materials [10][11][12][13]. Data from mercury porosimetry determinations are suitable for analysis from a fractal point of view.…”

Section: Introductionmentioning

confidence: 99%

“…Previous work by the author has shown how the application of these fractal analysis procedures are valuable tools not only for characterizing carbonaceous materials but for clarifying the phenomena underlying all the transformations of the materials. Fractal analysis is a complementary tool for analyzing textural characteristics and their evolution for series of oxidized coals [10,11], chars [12] and active carbons [13]. This analysis complements traditional approaches for clarifying the mechanisms of pore development during the steps of coal oxidation, coal carbonization and char activation.…”

Section: Introductionmentioning

confidence: 99%

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Textural changes during CO2 activation of chars: A fractal approach

Mahamud

2007

Applied Surface Science

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Section: Methods Used For the Determination Of Surface Fractal Dimensmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Textural changes during CO2 activation of chars: A fractal approach

Mahamud

2007

Applied Surface Science

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“…The FHH model is the most accurate and simplest method of calculating fractal dimensions, as follows [31].…”

Section: Fractal Dimension From Lp-n 2 -Ad Isothermsmentioning

confidence: 99%

“…Therefore, other investigations should be conducted. The MIP method is widely used for analyzing the structural characteristics of the seepage pores of porous materials [15,31]. Thus, this method was applied in this study to test the structural properties of the seepage pores and their fractal characteristics.…”

Section: Fractal Characteristics Of the Adsorbed Pores From Raw And Lmentioning

confidence: 99%

Damage Effects and Fractal Characteristics of Coal Pore Structure during Liquid CO2 Injection into a Coal Bed for E-CBM

Wei

et al. 2018

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Pore structure has a significant influence on coal-bed methane (CBM) enhancement. Injecting liquid CO 2 into coal seams is an effective way to increase CBM recovery. However, there has been insufficient research regarding the damage effects and fractal characteristics of pore structure at low temperature induced by injecting liquid CO 2 into coal samples. Therefore, the methods of low-pressure nitrogen adsorption-desorption (LP-N 2 -Ad) and mercury intrusion porosimetry (MIP) were used to investigate the damage effects and fractal characteristics of pore structure with full aperture as the specimens were frozen by liquid CO 2 . The adsorption isotherms revealed that the tested coal samples belonged to type B, indicating that they contained many bottle and narrow-slit shaped pores. The average pore diameter (APD; average growth rate of 18.20%), specific surface area (SSA; average growth rate of 7.38%), and total pore volume (TPV; average growth rate of 18.26%) increased after the specimens were infiltrated by liquid CO 2 , which indicated the generation of new pores and the transformation of original pores. Fractal dimensions D 1 (average of 2.58) and D 2 (average of 2.90) of treated coal samples were both larger the raw coal (D 1 , average of 2.55 and D 2 , average of 2.87), which indicated that the treated specimens had more rough pore surfaces and complex internal pore structures than the raw coal samples. The seepage capacity was increased because D 4 (average of 2.91) of the treated specimens was also higher than the raw specimens (D 4 , average of 2.86). The grey relational coefficient between the fractal dimension and pore structure parameters demonstrated that the SSA, APD, and porosity positively influenced the fractal features of the coal samples, whereas the TPV and permeability exerted negative influences.

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Classification of the tight oil reservoir storage space in the Raoyang Sag of the Jizhong Depression in the Bohai Bay Basin, China

Chen

Zheng

et al. 2019

Energy Science & Engineering

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To improve the precision of predicting sweet spots in tight oil reservoirs, the classification of tight oil reservoir storage space is necessary. The tight oil reservoir in the lower submember of the first member of Shahejie Formation (Es1L) of the Raoyang Sag of the Jizhong Depression in the Bohai Bay Basin was selected as a case study for testing a new method of classifying tight oil reservoir storage space based on the results of porosity, permeability, and mercury intrusion porosimetry measurements. The pore‐throat size ranges were classified according to pore‐throat fractal characteristics and were divided into three groups: <100 nm, 100‐1000 nm, and >1000 nm. The oil‐bearing property of tight oil reservoir samples is closely related to the median and maximum pore‐throat radii. The inflection and intersection points in the trend lines of the pore volume percentages connected by different pore‐throat widths (<100 nm, 100‐1000 nm, and >1000 nm) vs the porosity and permeability values (which represent the relationship between the pore volume connectivity characteristics and porosity/permeability) were used as the basis for classifying the tight oil reservoir storage space. The tight oil reservoir storage space in the Es1L formation of the Raoyang Sag was divided into three categories, namely classes of III, II, and I. The porosity ranges of the three tight reservoir storage space classes are <5%, 5%‐8%, and 8%‐11%, respectively, and their permeability ranges are <0.04 mD, 0.04‐0.15 mD, and 0.15‐0.8 mD, respectively.

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Textural characterization of active carbons using fractal analysis

Cited by 23 publications

References 12 publications

Textural changes during CO2 activation of chars: A fractal approach

Textural changes during CO2 activation of chars: A fractal approach

Damage Effects and Fractal Characteristics of Coal Pore Structure during Liquid CO2 Injection into a Coal Bed for E-CBM

Classification of the tight oil reservoir storage space in the Raoyang Sag of the Jizhong Depression in the Bohai Bay Basin, China

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