Residence time distribution on flow characterisation of multichannel systems: Modelling and experimentation

Abstract

Residence Time Distribution (RTD) is a frequently used tool in conventional process equipment and it provides internal flow characterisation by simple tracer tests. In this paper, we explore the feasibility of using RTD to identify fluid distribution uniformity in millimetric multichannel devices. Both theoretical modelling and experimental implementation are conducted to 16-channel systems. Theoretical modelling confirms the effectiveness of non-intrusive RTD measurements in evaluating flowrate distribution uniformity. Different influencing factors, such as channel corrosion, blockage, distributor structure, channel length or width variation, etc., can be reflected by RTD response curve. The experimental setup consists of a lab-developed RTD test platform coupling a fast camera and two miniflowcells capable to quantify rapid tracer concentration evolution through carbon ink visualization. The platform is particularly powerful for very narrow RTD measurement with residence time down to 1 s. With the platform, we investigate the RTD characteristics of a multichannel device under several flow conditions. Model correlation of the experimental data gives valuable information such as fluid distribution, plug-flow ratio and perfectly mixed volume. 1.1. Previous studies Parallel millimetric channels can be configured to provide multiple functionalities including heat exchange, mixing and chemical reaction. Our previous studies [10,15,16] on a 16-channel device have demonstrated rapid mixing effect (with micromixing time down to 10 ms) and compact heat exchange property (with the overall heat transfer coefficient being in the range of 2000-5000 W m −2 K −1). All these performances are thanks to a special tree-like structure that serves as fluid distributor and collector. By using such a nature-inspired manifold, the flow distribution non-uniformity is estimated to be lower than 10% under test flow conditions. However, once channel flowrates differ among channels (non-uniform distribution, or maldistribution), the global performance is expected to degrade in most cases. Regarding micromixing and chemical reaction, uneven fluid distribution can result in unbalanced reagents composition thus totally different reaction kinetics. Shown in Fig. 1 is the link between fluid residence time and micromixing time, previously

Experimental methods in chemical engineering: Residence time distribution—RTD

“…The main applications in chemical engineering are fluidized and fixed bed, stirring tanks, heat exchangers, and distillation or absorption columns. In the mixing field, micro devices and microchannel by CFD and experiments are new applications where RTD is an essential approach to understand the global fluid dynamics . Coupling reactive gases with an inert tracer is an area of future research that could better evaluate system dynamics.…”

“…Together with experimental configuration, two other challenges are choosing appropriate tracers and an analyzer to detect them . Here we assess contributions to the variance introduced by the valves, the dead volumes in tubes, dwell time of the analyzer, the valve switch time, the contribution of the tail to the model best fit parameters, repeatability, and tracer.…”

“…For liquid phase applications, NaCl and KCl solutions are good tracers particularly with conductivity detectors . A second option is a colourful dye like blue indigo carmine that UV‐vis spectrophotometers and high speed cameras detect . Irradiating solids and detecting the gamma ray emission or bremsstrahlung radiation with scintillating NaI detectors is the most effective method for the solids phase .…”

“…We first evaluate the deviation of the fluid dynamics from ideal plug flow—a system for which all elements of the fluid remain in the vessel for the same amount time and travel in parallel paths with constraining walls . Chemical engineers develop hydrodynamic models based on the tracer profile and then apply them in reactor models to characterize the reaction rates, identify characteristic mixing phenomena for design, and optimization . Industrial researchers will be able to control and predict particle mixing as well as compare different options to maximize the profitability of the process and minimize waste.…”

“…Industrial researchers will be able to control and predict particle mixing as well as compare different options to maximize the profitability of the process and minimize waste. Moreover, combined with chemical kinetics, RTD analysis predicts chemical reaction conversion . Researchers apply RTD analysis to fixed and fluidized bed reactors, two phase stirred tanks, heat exchangers, distillation and absorption columns, chromatography columns, and trickle bed reactors …”