  Processes involving the manufacture of fine/bulk chemicals, pharmaceuticals,
biofuels, and waste treatment require plug flow characteristics to minimise
their energy consumption and costs, and maximise product quality. One such
versatile flow chemistry platform is the coiled tube reactor subjected to
oscillatory motion, producing excellent plug flow qualities equivalent to
well-mixed tanks-in-series 'N'. In this study, we discover the critical
features of these flows that result in high plug flow performance using a
data-driven approach. This is done by integrating Bayesian optimisation, a
surrogate model approach, with Computational fluid dynamics that we treat as a
black-box function to explore the parameter space of the operating conditions,
oscillation amplitude and frequency, and net flow rate. Here, we correlate the
flow characteristics as a function of the dimensionless Strouhal, oscillatory
Dean, and Reynolds numbers to the reactor plug flow performance value 'N'.
Under conditions of optimal performance (specific examples are provided
herein), the oscillatory flow is just sufficient to limit axial dispersion
through flow reversal and redirection, and to promote Dean vortices. This
automated, open-source, integrated method can be easily adapted to identify the
flow characteristics that produce an optimised performance for other chemical
reactors and processes.
