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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).
Optimizing complex reactor geometries is vital to promote enhanced efficiency. We present a framework to solve this nonlinear, computationally expensive, and derivative-free problem. Gaussian processes are used to learn a multi-fidelity model of reactor simulations correlating multiple continuous mesh fidelities. The search space of reactor geometries is explored through lower fidelity simulations, evaluated based on a weighted acquisition function, trading off information gain with cost. Within our framework, DARTS, we derive a novel criteria for dictating optimization termination, ensuring a high fidelity solution is returned before budget is exhausted. We investigate the design of helical-tube reactors under pulsed-flow conditions, which have demonstrated outstanding mixing characteristics. To validate our results, we 3D print and experimentally validate the optimal reactor geometry, confirming mixing performance. Our approach is applicable to a broad variety of expensive simulation-based optimization problems, enabling the design of novel parameterized chemical reactors.
Author(s): Savage T, Basha N, McDonough J, Matar OK, del Rio Chanona EA
Publication type: Article
Publication status: Published
Journal: Computers & Chemical Engineering
Year: 2023
Volume: 179
Pages: 108410
Online publication date: 16/09/2023
Acceptance date: 07/09/2023
Date deposited: 21/11/2023
ISSN (print): 0098-1354
ISSN (electronic): 1873-4375
Publisher: Elsevier Ireland Ltd.
URL: https://doi.org/10.1016/j.compchemeng.2023.108410
DOI: 10.1016/j.compchemeng.2023.108410
ePrints DOI: 10.57711/mp4x-2a65
Data Access Statement: All code to generate results is available at the following GitHub repository https://github.com/OptiMaL-PSE-Lab/pulsed-reactor-optimisation.
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