Multi-objective Bayesian optimization of chemical reactor design using computational fluid dynamics

Seongeon Park, Jonggeol Na, Minjun Kim, Jong Min Lee

Research output: Contribution to journalArticlepeer-review

63 Scopus citations

Abstract

This study presents a computational fluid dynamics (CFD) based optimal design tool for chemical reactors, in which multi-objective Bayesian optimization (MBO) is utilized to reduce the number of required CFD runs. Detailed methods used to automate the process by connecting CFD with MBO are also proposed. The developed optimizer was applied to minimize the power consumption and maximize the gas holdup in a gas-sparged stirred tank reactor, which has six design variables: the aspect ratio of the tank, the diameter and clearance of each of the two impellers, and the gas sparger. The saturated Pareto front is obtained after 100 iterations. The resulting Pareto front consists of many near-optimal designs with significantly enhanced performances compared to conventional reactors reported in the literature. We anticipate that this design approach can be applied to any process unit design problems that require a large number of CFD simulation runs.

Original languageEnglish
Pages (from-to)25-37
Number of pages13
JournalComputers and Chemical Engineering
Volume119
DOIs
StatePublished - 2 Nov 2018

Bibliographical note

Publisher Copyright:
© 2018 Elsevier Ltd

Keywords

  • Bayesian optimization
  • CFD-based optimization
  • Computational fluid dynamics
  • Machine learning
  • Multi-objective optimization
  • Reactor design

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