按比例缩小的沼气生产搅拌沼气池的功率消耗和流体混合

IF 4.3 Q2 ENGINEERING, CHEMICAL
Federico Alberini, Francesco Maluta, Alessandro Paglianti and Giuseppina Montante*, 
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引用次数: 1

摘要

采用拟塑性流体模型对沼气生产中典型的非常规搅拌槽进行了实验研究。基于Metzner-Otto方法的流体表观粘度范围为39 ~ 264 mPa·s,旋转雷诺数范围为17 ~ 648。采用应变计技术测量了三个顶部进入搅拌器的功率消耗,并得到了从层流到完全湍流的全流型的功率数曲线。通过粒子图像测速法测量的流场,我们可以观察到不同工况下的流体循环模式及其变化。测量结果显示,相对较低的轴向和径向速度,特别是在储罐底部,可能会阻碍固体原料悬浮和随后的沼气生产。在叶轮转速和混合物粘度变化不大的情况下,可以观察到流动模式的显著变化。通过平面激光诱导荧光获得的示踪剂的均匀化动力学使我们可以估计无量纲混合时间,这一趋势与传统搅拌容器几何形状的趋势相似。通过不同技术组合收集的详细流体动力学信息有助于优化能量需求,并避免由于流体混合不良而导致的沼气生产失败。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Power Consumption and Fluid Mixing in a Scale-Down Geometry of a Stirred Digester for Biogas Production

Power Consumption and Fluid Mixing in a Scale-Down Geometry of a Stirred Digester for Biogas Production

A unconventional stirred tank of geometry typically adopted for the production of biogas is experimentally investigated with pseudo-plastic model fluids. The apparent viscosities of the fluids, based on the Metzner–Otto method, are in the range of 39–264 mPa·s, resulting in a range of rotational Reynolds number equal to 17–648. The power consumption of the three top-entering agitators is measured by a strain gauge technique, and the power number curve is obtained in the full range of flow regimes, going from laminar to fully turbulent conditions. The flow field measured by particle image velocimetry allows us to observe the fluid circulation patterns and their variations in different operative conditions. The measurements reveal relatively low axial and radial velocities, especially toward the bottom of the tank, that may hinder solid feedstock suspension and subsequent biogas production. Significant changes in the flow patterns are observed with small variations in the impeller speed and the mixture viscosity. The homogenization dynamics of a tracer obtained by planar laser-induced fluorescence leads us to estimate the dimensionless mixing time, a trend similar to that observed for conventional stirred vessel geometries. The detailed fluid dynamics information collected by a combination of different techniques can contribute to optimize the energy requirement and to avoid failure of the biogas production due to poor fluid mixing.

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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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