Scaling Up 3D-Printed Porous Reactors for the Continuous Synthesis of 2,5-Diformylfuran

IF 4.3 Q2 ENGINEERING, CHEMICAL ACS Engineering Au Pub Date : 2023-12-18 DOI:10.1021/acsengineeringau.3c00064
Dionysia Koufou,  and , Simon Kuhn*, 
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Abstract

The present study investigates the potential for scaling up 3D-printed porous reactors at the millimeter scale by integrating different reactor configurations in series. These reactor configurations, ranging from a single reactor (N = 1) to six reactors in series (N = 6), were evaluated for their performance in terms of axial dispersion in a gas–liquid system, with a focus on identifying potential dead zones. The scaled-up reactor systems exhibited a reduced deviation from plug flow behavior, mainly attributed to improved radial mixing maintained throughout the entire length of the porous structures. Among the various configurations tested, the scaled-up system featuring six reactors displayed the highest coefficient of variation (CoV) at approximately 24% for residence times exceeding 100 s. In all cases, the presence of stagnant zones influenced the shape of the residence time distribution (RTD) curves, although in the scaled-up system these stagnant zones did not significantly impact the overall performance or the yield of 2,5-diformylfuran (DFF). This was due to the narrow RTD and effective mass transfer between the stagnant and active flow compartments. Notably, the selectivity remained at 100%, and the highest yield of DFF (approximately 81%) was achieved for a residence time of 6.61 min in the scaled-up system. Despite introducing mass transfer limitations when operating at the millimeter scale, the scaled-up system achieved DFF productivity levels comparable to microreaction systems at significantly lower energy dissipation.

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放大三维打印多孔反应器以连续合成 2,5-二甲酰基呋喃
本研究通过将不同的反应器配置串联起来,探讨了在毫米尺度上放大三维打印多孔反应器的潜力。这些反应器配置从单个反应器(N = 1)到六个串联反应器(N = 6)不等,评估了它们在气液系统中的轴向分散性能,重点是识别潜在的死区。按比例放大的反应器系统显示出较小的塞流偏差,这主要归因于在多孔结构的整个长度上保持了较好的径向混合。在测试的各种配置中,由六个反应器组成的放大系统在停留时间超过 100 秒时显示出最高的变异系数(CoV),约为 24%。在所有情况下,停滞区的存在都会影响停留时间分布(RTD)曲线的形状,不过在放大系统中,这些停滞区并未对整体性能或 2,5-二甲酰基呋喃(DFF)的产量产生显著影响。这是由于滞留区和活性流区之间的 RTD 较窄且有效传质。值得注意的是,在放大的系统中,选择性保持在 100%,停留时间为 6.61 分钟时,DFF 的产率最高(约 81%)。尽管在毫米尺度下运行时会引入传质限制,但放大系统以显著较低的能量消耗达到了与微反应系统相当的 DFF 产率水平。
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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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