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Lookup NU author(s): Dr Jonathan McDonough
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
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 parameterizations of reactor geometries are low dimensional with expensive optimization, limiting more complex solutions. To address this challenge, we have established a machine learning-assisted approach for the design of new chemical reactors, combining the application of high-dimensional parameterizations, computational fluid dynamics and multi-fidelity Bayesian optimization. We associate the development of mixing-enhancing vortical flow structures in coiled reactors with performance and used our approach to identify the key characteristics of optimal designs. By appealing to the principles of fluid dynamics, we rationalized the selection of design features that lead to experimental plug flow performance improvements of ~60% compared with conventional designs. Our results demonstrate that coupling advanced manufacturing techniques with ‘augmented intelligence’ approaches can give rise to reactor designs with enhanced performance.
Author(s): Savage T, Basha N, McDonough J, Krassowski J, Matar O, del Rio-Chanona EA
Publication type: Article
Publication status: Published
Journal: Nature Chemical Engineering
Year: 2024
Volume: 1
Pages: 522–531
Print publication date: 01/09/2024
Online publication date: 05/08/2024
Acceptance date: 28/06/2024
Date deposited: 06/09/2024
ISSN (electronic): 2948-1198
Publisher: Nature Publishing Group
URL: https://doi.org/10.1038/s44286-024-00099-1
DOI: 10.1038/s44286-024-00099-1
Data Access Statement: The OpenFOAM case files for all four reactors presented in Fig. 1 are available via GitHub at https://github.com/OptiMaL-PSE-Lab/pulsed-reactor-optimisation. The STL files for the reactors presented in Fig. 3 are available in the Supplementary Information. Source data are provided with this paper. All code used within this study can be found within the associated repository https://github.com/OptiMaL-PSE-Lab/pulsed-reactor-optimisation. For use as an optimization benchmark problem, the reactor simulations are also available in the form of a Docker-based REST API with code and instructions at https://github.com/trsav/reactor_benchmark.
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