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Discovery of mixing characteristics for enhancing coiled reactor performance through a Bayesian optimisation-CFD approach

Lookup NU author(s): Dr Jonathan McDonough

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

Plug flow characteristics are advantageous in various manufacturing processes for fine/bulk chemicals, pharmaceuticals, biofuels, and waste treatment as they contribute to maximising product yield. 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.


Publication metadata

Author(s): Basha N, Savage T, McDonough J, del Río Chanona EA, Matar OK

Publication type: Article

Publication status: Published

Journal: Chemical Engineering Journal

Year: 2023

Volume: 473

Pages: 145217

Print publication date: 01/10/2023

Online publication date: 06/08/2023

Acceptance date: 02/08/2023

Date deposited: 22/08/2023

ISSN (print): 1385-8947

ISSN (electronic): 1873-3212

Publisher: Elseiver

URL: https://doi.org/10.1016/j.cej.2023.145217

DOI: 10.1016/j.cej.2023.145217

Data Access Statement: Data will be made available on request.


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Funding

Funder referenceFunder name
EP/T000414/1
EPSRC

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