Stylianos Kyrimis, Matthew E. Potter, Robert Raja and Lindsay-Marie Armstrong*,
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引用次数: 0
Abstract
Computational fluid dynamics (CFD) modeling plays a pivotal role in optimizing fixed bed catalytic chemical reactors to enhance performance but must accurately capture the various length- and time-scales that underpin the complex particle–fluid interactions. Within catalytic particles, a range of pore sizes exist, with micro-pore scales enhancing the active surface area for increased reactivity and macro-pore scales enhancing intraparticle heat and mass transfer through intraparticle convection. Existing particle-resolved CFD models primarily approach such dual-scale particles with low intraparticle macro-porosities as purely solid. Consequently, intraparticle phenomena associated with intraparticle convection are neglected, and their impact in the full bed scale is not understood. This study presents a porous particle CFD model, whereby individual particles are defined through two distinct porosity terms, a macro-porosity term responsible for the particle’s hydrodynamic profile and a micro-porosity term responsible for diffusion and reaction. By comparing the flow profiles through full beds formed by porous and solid particles, the impact of intraparticle convection on mass and heat transfer, as well as on diffusion and reaction, was investigated.
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)