Additive manufacturing has enabled the production of more advanced reactor
geometries, resulting in the potential for significantly larger and more
complex design spaces. Identifying and optimising promising configurations
within broader design spaces presents a significant challenge for existing
human-centric design approaches. As such, existing parameterisations of
coiled-tube reactor geometries are low-dimensional with expensive optimisation
limiting more complex solutions. Given algorithmic improvements and the onset
of additive manufacturing, we propose two novel coiled-tube parameterisations
enabling the variation of cross-section and coil path, resulting in a series of
high dimensional, complex optimisation problems. To ensure tractable, non-local
optimisation where gradients are not available, we apply multi-fidelity
Bayesian optimisation. Our approach characterises multiple continuous
fidelities and is coupled with parameterised meshing and simulation, enabling
lower quality, but faster simulations to be exploited throughout optimisation.
Through maximising the plug-flow performance, we identify key characteristics
of optimal reactor designs, and extrapolate these to produce two novel
geometries that we 3D print and experimentally validate. By demonstrating the
design, optimisation, and manufacture of highly parameterised reactors, we seek
to establish a framework for the next-generation of reactors, demonstrating
that intelligent design coupled with new manufacturing processes can
significantly improve the performance and sustainability of future chemical
processes.Comment: 11 pages, 8 figure