Abstract
The ambient air vaporizer (AAV) using ambient air as heat source was environment-friendly method to vaporize LNG. The main problem of the vaporizer is performance degradation caused by frost formation on surface of tube. Determining the appropriate value of design variables under given climate is a critical issue. In this paper, design optimization in the vaporizer is performed using DIRECT algorithm whose result is compared with the result obtained by GA. Time series dataset including temperature and relative humidity of air for a year is categorized using k-means clustering. Finally, the optimal design shows 23.4 % improved performance of AAV and defrosting time is drastically reduced.
Original language | English |
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Title of host publication | Computer Aided Chemical Engineering |
Publisher | Elsevier B.V. |
Pages | 1795-1800 |
Number of pages | 6 |
DOIs | |
State | Published - 1 Jan 2018 |
Publication series
Name | Computer Aided Chemical Engineering |
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Volume | 44 |
ISSN (Print) | 1570-7946 |
Bibliographical note
Publisher Copyright:© 2018 Elsevier B.V.
Keywords
- Ambient air vaporizer
- DIRECT algorithm
- K-means clustering