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 language | English |
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Pages (from-to) | 25-37 |
Number of pages | 13 |
Journal | Computers and Chemical Engineering |
Volume | 119 |
DOIs | |
State | Published - 2 Nov 2018 |
Bibliographical note
Funding Information:This work was supported by the Energy Efficiency & Resources Core Technology Program of the Korea Institute of Energy Technology Evaluation and Planning(KETEP) granted financial resource from the Ministry of Trade, Industry & Energy (MOTIE), Republic of Korea (No. 20152010201850 ). This research was respectfully supported by Engineering Development Research Center (EDRC) funded by the Ministry of Trade, Industry & Energy (MOTIE). (No. N0000990 ).
Funding Information:
This work was supported by the Energy Efficiency & Resources Core Technology Program of the Korea Institute of Energy Technology Evaluation and Planning(KETEP) granted financial resource from the Ministry of Trade, Industry & Energy(MOTIE), Republic of Korea (No. 20152010201850). This research was respectfully supported by Engineering Development Research Center (EDRC) funded by the Ministry of Trade, Industry & Energy (MOTIE). (No. N0000990).
Publisher Copyright:
© 2018 Elsevier Ltd
Keywords
- Bayesian optimization
- CFD-based optimization
- Computational fluid dynamics
- Machine learning
- Multi-objective optimization
- Reactor design