Reaction Rates and Selectivity in Catalyst Pores

Wheeler, Ahlborn

doi:10.1016/s0360-0564(08)60109-1

Cited by 398 publications

(173 citation statements)

References 26 publications

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“…The "unipore" diffusion model, which is also known as the "parallel pore" model, was developed by Wheeler [32] for use with porous catalyst pellets that exhibit well-defined pore structures and has been applied to model the gas diffusion process and calculate the lost gas content of coal seams by some researchers [21,22,26,27]. This model assumes that the porous solid is interspersed with non-connecting cylindrical pores with identical radii, and the gas concentration at the surface of the spheres throughout the adsorption process is constant.…”

Section: Unipore Diffusion Modelmentioning

confidence: 99%

Methane Adsorption Rate and Diffusion Characteristics in Marine Shale Samples from Yangtze Platform, South China

et al. 2017

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Knowledge of the gas adsorption rate and diffusion characteristics in shale are very important to evaluate the gas transport properties. However, research on methane adsorption rate characteristics and diffusion behavior in shale is not well established. In this study, high-pressure methane adsorption isotherms and methane adsorption rate data from four marine shale samples were obtained by recording the pressure changes against time at 1-s intervals for 12 pressure steps. Seven pressure steps were selected for modelling, and three pressure steps of low (~0.4 MPa), medium (~4.0 MPa), and high (~7.0 MPa) were selected for display. According to the results of study, the methane adsorption under low pressure attained equilibrium much more quickly than that under medium and high pressure, and the adsorption rate behavior varied between different pressure steps. By fitting the diffusion models to the methane adsorption rate data, the unipore diffusion model based upon unimodal pore size distribution failed to describe the methane adsorption rate, while the bidisperse diffusion model could reasonably describe most of the experimental adsorption rate data, with the exception of sample YY2-1 at high pressure steps. This phenomenon may be related to the restricted assumption on pore size distribution and linear adsorption isotherm. The diffusion parameters α and β/α obtained from the bidisperse model indicated that both macro-and micropore diffusion controlled the methane adsorption rate in shale samples, as well as the relative importance and influence of micropore diffusion and adsorption to adsorption rate and total adsorption increased with increasing pressure. This made the inflection points, or two-stage process, at higher pressure steps not as evident as at low pressure steps, and the adsorption rate curves became less steep with increasing pressure. This conclusion was also supported by the decreasing difference values with increasing pressures between macro-and micropore diffusivities obtained using the bidisperse model, which is roughly from 10 −3 to 10 0 , and 10 −3 to 10 −1 , respectively. Additionally, an evident negative correlation between macropore diffusivities and pressure lower than 3-4 MPa was observed, while the micropore diffusivities only showed a gentle decreasing trend with pressure. A mirror image relationship between the variation in the value of macropore diffusivity and adsorption isotherms was observed, indicating the negative correlation between surface coverage and gas diffusivity. The negative correlation of methane diffusivity with pressure and surface coverage may be related to the increasing degree of pore blockage and the decreasing concentration gradient of methane adsorption. Finally, due to the significant deviation between the unipore model and experimental adsorption rate data, a new estimation method based upon the bidisperse model is proposed here.

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Section: Unipore Diffusion Modelmentioning

confidence: 99%

Methane Adsorption Rate and Diffusion Characteristics in Marine Shale Samples from Yangtze Platform, South China

et al. 2017

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“…Specific surface area (SBET) was calculated from the first part of the isotherm (P/Po < 0.3) using 0.162 nm 2 as the molecular area of adsorbed N 2 molecule. Poresize distribution was obtained taking into account considerations of Wheeler ( 1955), Barret et al (1951), Pierce (1953), and Orr and Dalla Valle (1953). The t-plot was made according to Lippens and De Boer (1965).…”

Section: ~ (2(9)mentioning

confidence: 99%

Hydroxy-Chromium Smectite

Volzone¹,

Cesio²,

Sánchez³

et al. 1993

Clays and clay miner.

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Abstract--Hydroxy-chromium solutions were prepared from chromium nitrate solutions by adding NaOH with OH/Cr = 2.The solutions were treated at 20~ and 60~ The hydrolysis "times were from 1 to 100 days. Polymeric species in hydrolyzed chromium solutions were followed by visible absorption spectra within the range 325-800 nm and by pH measurement. OH-Cr-smectite with high d(001) spacing (2.07 nm) was obtained when hydroxy-chromium solution was prepared at 60~ and with 1-day hydrolysis. When this sample was heated up to 350~ the basal spacing collapsed at 1.8 nm.The samples were characterized by X-ray diffraction and N2 adsorption-desorption isotherms.

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“…Two pore models were implemented into PREKIN: the parallel pore model and the random pore model. The parallel model 54 uses the porosity and tortuosity to obtain effective diffusivity as in the following equation:…”

Section: Single-film Char Oxidationmentioning

confidence: 99%

A Computational Workbench Environment for Virtual Power Plant Simulation

Bockelie¹,

Swensen²,

Denison³

et al. 2004

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This is the thirteenth Quarterly Technical Report for DOE Cooperative Agreement No: DE-FC26-00NT41047. The goal of the project is to develop and demonstrate a Virtual Engineeringbased framework for simulating the performance of Advanced Power Systems. Within the last quarter, good progress has been made on all aspects of the project. Software development efforts have focused on a preliminary detailed software design for the enhanced framework. Given the complexity of the individual software tools from each team (i.e., Reaction Engineering International, Carnegie Mellon University, Iowa State University), a robust, extensible design is required for the success of the project. In addition to achieving a preliminary software design, significant progress has been made on several development tasks for the program. These include: 1) the enhancement of the controller user interface to support detachment from the Computational Engine and support for multiple computer platforms, 2) modification of the Iowa State University interface-to-kernel communication mechanisms to meet the requirements of the new software design, 3) decoupling of the Carnegie Mellon University computational models from their parent IECM (Integrated Environmental Control Model) user interface for integration with the new framework and 4) development of a new CORBA-based model interfacing specification. A benchmarking exercise to compare process and CFD based models for entrained flow gasifiers was completed. A summary of our work on intrinsic kinetics for modeling coal gasification has been completed. Plans for implementing soot and tar models into our entrained flow gasifier models are outlined. Plans for implementing a model for mercury capture based on conventional capture technology, but applied to an IGCC system, are outlined.

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Reaction Rates and Selectivity in Catalyst Pores

Cited by 398 publications

References 26 publications

Methane Adsorption Rate and Diffusion Characteristics in Marine Shale Samples from Yangtze Platform, South China

Methane Adsorption Rate and Diffusion Characteristics in Marine Shale Samples from Yangtze Platform, South China

Hydroxy-Chromium Smectite

A Computational Workbench Environment for Virtual Power Plant Simulation

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