Lee, Yongkyu Na, Jonggeol Lee, Won Bo 2024-01-19T21:33:12Z 2024-01-19T21:33:12Z 2021-09-05 2018-10-04 0098-1354 https://pubs.kist.re.kr/handle/201004/120805 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. (C) 2018 Elsevier Ltd. All rights reserved. English PERGAMON-ELSEVIER SCIENCE LTD HEAT-EXCHANGER NETWORKS LIQUEFIED NATURAL-GAS WAVELET TRANSFORM FROST GROWTH FLEXIBLE HEAT LAMINAR-FLOW OPTIMIZATION MODEL DENSIFICATION PERFORMANCE Robust design of ambient-air vaporizer based on time-series clustering Article 10.1016/j.compchemeng.2018.08.026 1 COMPUTERS & CHEMICAL ENGINEERING, v.118, pp.236 - 247 COMPUTERS & CHEMICAL ENGINEERING 118 236 247 scie scopus 000447783500017 2-s2.0-85052159389 Computer Science, Interdisciplinary Applications Engineering, Chemical Computer Science Engineering Article HEAT-EXCHANGER NETWORKS LIQUEFIED NATURAL-GAS WAVELET TRANSFORM FROST GROWTH FLEXIBLE HEAT LAMINAR-FLOW OPTIMIZATION MODEL DENSIFICATION PERFORMANCE Ambient air vaporizer Wavelet transform k-means clustering Feature extraction Robust design Global sensitivity analysis