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

Yongkyu Lee, Jonggeol Na, Won Bo Lee

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


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.

Original languageEnglish
Pages (from-to)236-247
Number of pages12
JournalComputers and Chemical Engineering
StatePublished - 4 Oct 2018

Bibliographical note

Publisher Copyright:
© 2018 Elsevier Ltd


  • Ambient air vaporizer
  • Feature extraction
  • Global sensitivity analysis
  • Robust design
  • Wavelet transform
  • k-means clustering


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