Park, Seongeon Na, Jonggeol Kim, Minjun Lee, Jong Min 2024-01-19T21:31:17Z 2024-01-19T21:31:17Z 2021-09-04 2018-11-02 0098-1354 https://pubs.kist.re.kr/handle/201004/120701 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. (C) 2018 Elsevier Ltd. All rights reserved. English PERGAMON-ELSEVIER SCIENCE LTD GAS-LIQUID FLOW GENETIC ALGORITHM RUSHTON TURBINE STIRRED VESSEL CFD DISPERSION COEFFICIENT SIMULATION MODEL Multi-objective Bayesian optimization of chemical reactor design using computational fluid dynamics Article 10.1016/j.compchemeng.2018.08.005 1 COMPUTERS & CHEMICAL ENGINEERING, v.119, pp.25 - 37 COMPUTERS & CHEMICAL ENGINEERING 119 25 37 scie scopus 000447645200003 2-s2.0-85052942007 Computer Science, Interdisciplinary Applications Engineering, Chemical Computer Science Engineering Article GAS-LIQUID FLOW GENETIC ALGORITHM RUSHTON TURBINE STIRRED VESSEL CFD DISPERSION COEFFICIENT SIMULATION MODEL Multi-objective optimization Bayesian optimization Computational fluid dynamics CFD-based optimization Reactor design Machine learning