(98e) The Effect of Scale-up on Mixing Efficiency in Oscillatory Flow Reactors Using Principal Component Based Analysis As a Novel Residence Time Distribution Measurement Tool

The effect of
scale-up on mixing efficiency in oscillatory flow reactors using principal
component based analysis as a novel residence time distribution measurement tool

Oscillatory flow
strategies through baffled tubular reactors provide an efficient approach in
improving process kinetics through enhanced micromixing and heat transfer¹. Known to have high surface area to volume ratios, oscillatory flow baffled
reactors (OFBR) generate turbulence by superimposing piston driven oscillatory
flow onto the net flow generated by a pump. By tuning the oscillating
parameters (amplitude and frequency), one can tailor the residence time
distribution of the system for a variety of multiphase applications^2,3,4. Using a microscope camera, principal
component image analysis, and pulse tracer injections, a novel noninvasive
approach has been developed to experimentally estimate dispersion coefficients
in two geometrically different systems (DN6 and DN15, Alconbury Weston Ltd.). Similarly,
a comprehensive experimental investigation of the effect of oscillation
parameters on the residence time distributions (RTD) is discussed in both
systems. The oscillation amplitude was found to have a significant positive
correlation with the dispersion coefficient with 1 mm providing the least
amount of dispersion in either system. Oscillation frequency had a less
significant impact on the dispersion coefficient, but optimal operation was
found to occur at 1.5 Hz for the DN6 and 1.0Hz for the DN15. Until now, OFBR
literature has not distinguished between piston and pump driven flow. Pump driven
flow was found to be ideal for both systems as it minimizes the measured
dispersion coefficient. However, piston driven turbulence is essential for
avoiding particle settling in two phase (solid-liquid) systems and should be
considered in two phase applications such as crystallization.

To further
understand how operating parameters affect system flow conditions, a
computational fluid dynamics model was developed. Found to have good agreement
with experimental validation, the CFD model further explains how system
geometry and oscillation conditions drastically affect the size and shape of
eddies generated, ultimately impacting the measured dispersion coefficient. The
CFD model also sheds light on how the RTD of the system is (indirectly)
dependent on temperature and mass diffusivity. Using a combined experimental
and computational approach, optimal operating conditions were determined for
each system. References

1.     
Stonestreet, P., & Harvey, A.
P. (2002). A Mixing-Based Design Methodology for Continuous Oscillatory Flow
Reactors. Chemical Engineering Research and Design, 80(January),
31–44.

2.     
Kacker, R., Regensburg, S. I.,
& Kramer, H. J. M. (2017). Residence time distribution of dispersed liquid
and solid phase in a continuous oscillatory flow baffled crystallizer. Chemical
Engineering Journal, 317, 413–423.

3.     
Dickens, A. W., Mackley, M. R.,
& Williams, H. R. (1989). Experimental residence time distribution
measurements for unsteady flow in baffled tubes. Chemical Engineering
Science, 44(7), 1471–1479.

4.     
Ni, X., & Pereira, N. E.
(2000). Parameters Affecting Fluid Dispersion in a Continuous Oscillatory
Baffled Tube. AIChE Journal, 46(1), 37–45.