Machine Learning-Assisted Discovery of Flow Reactor Designs
This work addresses the problem of optimizing complex reactor designs for chemical engineering, leading to potential emissions reduction and sustainability benefits, though it is incremental as it builds on existing methods like Bayesian optimization.
The researchers tackled the challenge of designing advanced chemical reactors by developing a machine learning-assisted approach that combines high-dimensional parameterizations, computational fluid dynamics, and multi-fidelity Bayesian optimization, resulting in experimental plug flow performance improvements of 60% over conventional designs.
Additive manufacturing has enabled the fabrication of advanced reactor geometries, permitting larger, more complex design spaces. Identifying promising configurations within such spaces presents a significant challenge for current approaches. Furthermore, existing parameterisations of reactor geometries are low-dimensional with expensive optimisation limiting more complex solutions. To address this challenge, we establish a machine learning-assisted approach for the design of the next-generation of chemical reactors, combining the application of high-dimensional parameterisations, computational fluid dynamics, and multi-fidelity Bayesian optimisation. We associate the development of mixing-enhancing vortical flow structures in novel coiled reactors with performance, and use our approach to identify key characteristics of optimal designs. By appealing to the principles of flow dynamics, we rationalise the selection of novel design features that lead to experimental plug flow performance improvements of 60% over conventional designs. Our results demonstrate that coupling advanced manufacturing techniques with `augmented-intelligence' approaches can lead to superior design performance and, consequently, emissions-reduction and sustainability.