The gas-liquid flow behavior through a milli-scaled channel provided with a staggered herringbone-like static mixer was investigated using high-speed recordings. For three different substance systems consisting of water, 5 wt % acetic acid in aqueous solution, and propylene glycol as liquid phase and nitrogen as gas phase, flow patterns and their transitions were determined by analyzing image sequences of the flow and summarized in flow pattern maps. Surge flow, slug flow, and bubbly flow were observed at different flow rates. The flow distribution and transitions between flow patterns mainly depend on the viscosity and surface tension of the liquid phase. By just reducing the surface tension, slug flow is not observed, and thus an early transition into a bubbly flow regime takes place. An increase in viscosity counteracts this effect.
A compartment modeling approach based on computational fluid dynamics (CFD) simulations is applied to a simplified static mixer geometry. Compartments are derived from velocity fields obtained from cold CFD simulations. This methodology is based on the definition of periodic flow zones (PFZ) derived from the recurrent flow profile within the static mixer. In general, PFZ can be characterized by two different compartments: flow zones with hydrodynamic behavior of a tubular reactor and dead zones exhibiting a more continuous stirred tank reactor‐like characteristic. In CFD studies the influence of changing fluid properties, for example viscosity, on flow profile due to polymerization progress is considered. In the deterministic compartment model, the continuous flow profile within the static mixer is transformed to basic reactor models interconnected via an exchange stream. To reduce model complexity and the number of model parameters, constant volumes of compartments are assumed. Changes in hydrodynamics are considered by a variable exchange flow rate as a function of Re manipulating residence time in compartments. Simulation studies show the influence of decreasing exchange flow rates with polymerization progress, as Re decreases, resulting in a greater increase of viscosity in dead zones. The reactor performance is qualitatively represented by the simulation results.
An apparatus with rectangular product channels and static mixing elements for process intensification is investigated in terms of heat transfer and hydrodynamics. Herringbone-like static mixing elements increase the overall heat transfer coefficient significantly, and the stall angle was found to have a major influence on hydrodynamics and heat transfer. The knowledge of structural parameters of the mixing elements is crucial for apparatus design and prediction of the apparatus performance. A method to derive mean structural parameters from experimental results is presented.
Two Machine Learning algorithms -LASSO and Random Forest -are applied to derive regression models for the prediction of gas bubble diameters using supervised learning techniques. Experimental data obtained from wire-mesh sensor (WMS) measurements in a deionized water/air system serve as the data base. Python libraries are used to extract features characterizing WMS measurement signals of single passing bubbles. Prediction accuracy is largely increased with the obtained regression models, compared to well-established methods to predict bubble sizes based on WMS measurements.
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