稀释催化固定床反应器甲醛生产的颗粒解析CFD模拟

IF 4.3 Q2 ENGINEERING, CHEMICAL
Martin Kutscherauer*,  and , Gregor D. Wehinger*, 
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引用次数: 0

摘要

在高放热反应的催化固定床反应器中,通常用惰性颗粒稀释床层,以防止热失控,并使反应沿反应器长度分布更均匀。甲醇部分氧化制甲醛是一个具有高度工业相关性的例子,其中应用了稀释的固定床。在这项工作中,粒子解析计算流体动力学(PRCFD)模拟了一个工业规模的甲醛生产固定床的热点区域(0-0.5 m),系统地研究了稀释对整体反应器性能和局部分布数量(如温度和催化剂有效性因子)的影响。PRCFD是稀释固定床层模拟最详细的建模方法,因为固定床层几何形状的空间分辨率允许直接考虑惰性颗粒,而无需执行平均活度因子。不同的催化剂分布对积分转化、热点形成和催化剂过热有显著的影响,而增加惰性导热系数对热传递和反应的影响较小。两种不同催化剂布置方式的最高催化剂温度相差可达34 K。最后,本研究表明,由于催化剂过热(ΔT = 23.12 K)和孔扩散限制(ηi,FA <;0.5)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Particle-Resolved CFD Simulation of Diluted Catalytic Fixed Bed Reactors for Formaldehyde Production

In catalytic fixed bed reactors for highly exothermic reactions, the bed is often diluted with inert particles to prevent thermal runaway and to distribute the reaction more homogeneously along the reactor length. The partial oxidation of methanol to formaldehyde is an example with high industrial relevance, in which diluted fixed beds are applied. In this work, particle-resolved computational fluid dynamics (PRCFD) simulations are conducted for the hotspot region (0–0.5 m) of an industrial scale fixed bed for formaldehyde production to systematically investigate the impact of dilution on integral reactor performance and locally distributed quantities, such as the temperature and catalyst effectiveness factor. PRCFD is the most detailed modeling approach for the simulation of diluted fixed beds since the spatial resolution of the fixed bed geometry allows the inert particles to be considered directly without the implementation of averaged activity factors. Different catalyst distributions have a significant effect on integral conversion, hotspot formation, and catalyst overheating while increasing the inert thermal conductivity has only a minor impact on heat transport and hence reaction. The difference between the maximum catalyst temperature of two different catalyst arrangements can reach 34 K. Finally, the present study demonstrates that even highly diluted fixed beds with industrial particle and tube dimensions are not suited to perform intrinsic kinetic measurements for the partial oxidation of methanol because of catalyst overheating (ΔT = 23.12 K) and pore diffusion limitation (ηi,FA < 0.5).

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来源期刊
ACS Engineering Au
ACS Engineering Au 化学工程技术-
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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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