Computational fluid dynamic enabled design optimisation of miniaturised continuous oscillatory baffled reactors in chemical processing

Abstract

The first CFD-enabled multi-objective design optimisation methodology for continuous oscillatory baffled reactors (COBRs), used for flow chemistry-based process development, is described, where performance is quantified in terms of two metrics: a mixing efficiency index and the variance of the residence time distribution. The effect of cross-validation approaches on the surrogate modelling of these performance metrics is examined in detail and the resultant surrogate models used to demonstrate the influence of key design variables. Pareto fronts of non-dominated solutions are presented to illustrate the available design compromises for COBR performance and it is shown that these can give a narrow Residence Time Distribution and good mixing within the final design. The novel feature of offset baffles within a channel, explored here for the first time, is identified as a key parameter in improving the performance of COBRs.

KEYWORDS:

• Continuous oscillatory baffled reactor
• surrogate modelling
• multi-objective optimisation
• machine learning

Acknowledgements

This work was undertaken on ARC3, part of the High Performance Computing facilities at the University of Leeds, UK.

Disclosure statement

No potential conflict of interest was reported by the authors.