Robust design of ambient-air vaporizer based on time-series clustering

Title
Robust design of ambient-air vaporizer based on time-series clustering
Authors
나종걸이용규이원보
Issue Date
2018-10
Publisher
Computers & chemical engineering
Citation
VOL 118-247
Abstract
A methodology for the robust design of an ambient-air vaporizer under time-series weather conditions is proposed. Two techniques are used to extract representative features in the time-series data. (i) The major trend of a day is rapidly identified by the discrete wavelet transform (DWT), in which a high level of Haar function reflects the trend of a day and drastically reduces the data size. (ii) The k-means clustering method groups the similar features of a year, and the reconstructed time-series dataset extracted by the centroids of clusters represents the weather conditions of a year. The results of the multi-feature-based optimization were compared with non-wavelet based and multi-period optimization by simulation under a year of data. The design structure from the feature extraction shows 22.92% better performance than the original case and is 12 times more robust in different weather conditions than clustering with raw data.
URI
http://pubs.kist.re.kr/handle/201004/68180
ISSN
0098-1354
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KIST Publication > Article
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