多孔介质开孔泡沫球固定床反应器的建模

IF 4.3 Q2 ENGINEERING, CHEMICAL
Govind Venaram Tak,  and , Himanshu Goyal*, 
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引用次数: 0

摘要

传统的固定床反应器使用的颗粒只允许物质通过扩散而不是对流传输。然而,使用高多孔颗粒,如开孔泡沫,允许大量气体通过它们。这样的固定床导致较低的压降和气固相之间的密切接触,这是催化反应和吸附过程所需要的。固定床建模的常用策略是使用多孔介质假设,其中气相和固相表示为有效的多孔介质。这种方法需要使用简单几何的解析关系和复杂几何的经验关联来计算几个有效的性质。然而,对于高多孔颗粒的固定床,这种表示是不可用的。本研究通过开发一个以开孔泡沫颗粒为多孔介质的固定床反应器的数学框架来解决这个问题。为此,采用了体积平均和渐近平均技术。基于体积平均技术建立了多孔介质(连续介质)模型的控制方程,并利用单元胞模拟计算了有效性质。开发的数学框架是对线性和非线性催化动力学和CO2吸附的三维粒子解析模拟进行评估的。对于所有的测试用例,所开发的框架可以再现由颗粒分解模拟预测的压降和物种浓度,并且模拟时间降低了几个数量级。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modeling Fixed Bed Reactors of Open-Cell Foam Pellets as Porous Media

Traditional fixed-bed reactors use pellets that only allow species transport through diffusion, not convection. However, using highly porous pellets, such as open-cell foams, allows bulk gas to move through them. Such a fixed bed results in a lower pressure drop and intimate contact between the gas and solid phases, which is desirable for catalytic reactions and adsorption processes. A common strategy for modeling fixed beds is to use the porous medium assumption, where the gas and solid phases are represented as an effective porous medium. This approach necessitates several effective properties calculated using analytical relations for simple geometries and empirical correlations for complex geometries. However, such a representation for a fixed bed of highly porous pellets is unavailable. This study addresses this problem by developing a mathematical framework for a fixed bed reactor of open-cell foam pellets as a porous medium. To this end, the volume averaging and asymptotic averaging techniques are employed. The governing equations for the porous medium (continuum) model are developed based on the volume averaging technique, and the effective properties are calculated using the unit cell simulations. The developed mathematical framework is assessed against three-dimensional particle-resolved simulations for linear and nonlinear catalytic kinetics and CO2 adsorption. For all the test cases, the developed framework can reproduce the pressure drop and species concentration predicted by the particle-resolved simulations with orders of magnitude reduction in the simulation time.

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来源期刊
ACS Engineering Au
ACS Engineering Au 化学工程技术-
自引率
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期刊介绍: )ACS Engineering Au is an open access journal that reports significant advances in chemical engineering applied chemistry and energy covering fundamentals processes and products. The journal's broad scope includes experimental theoretical mathematical computational chemical and physical research from academic and industrial settings. Short letters comprehensive articles reviews and perspectives are welcome on topics that include:Fundamental research in such areas as thermodynamics transport phenomena (flow mixing mass & heat transfer) chemical reaction kinetics and engineering catalysis separations interfacial phenomena and materialsProcess design development and intensification (e.g. process technologies for chemicals and materials synthesis and design methods process intensification multiphase reactors scale-up systems analysis process control data correlation schemes modeling machine learning Artificial Intelligence)Product research and development involving chemical and engineering aspects (e.g. catalysts plastics elastomers fibers adhesives coatings paper membranes lubricants ceramics aerosols fluidic devices intensified process equipment)Energy and fuels (e.g. pre-treatment processing and utilization of renewable energy resources; processing and utilization of fuels; properties and structure or molecular composition of both raw fuels and refined products; fuel cells hydrogen batteries; photochemical fuel and energy production; decarbonization; electrification; microwave; cavitation)Measurement techniques computational models and data on thermo-physical thermodynamic and transport properties of materials and phase equilibrium behaviorNew methods models and tools (e.g. real-time data analytics multi-scale models physics informed machine learning models machine learning enhanced physics-based models soft sensors high-performance computing)
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