화학공학소재연구정보센터
학회 한국화학공학회
학술대회 2017년 가을 (10/25 ~ 10/27, 대전컨벤션센터)
권호 23권 2호, p.1580
발표분야 공정시스템
제목 Fault detection of Ethylene Vinyl Acetate(EVA) reactor by using Dynamic Principal Component(DPCA) and Clustering Analysis
초록 Ethylene and Vinyl Acetate can make different grades by changing ratio of two reactants and process conditions. In this process, fault detection model for each grade is usually made by using PCA. However, the transition occurs too often that it is difficult to find normal condition in EVA reactor. If shut down can’t be predicted, then the plant have to be stop which causes economical loss. So a fault detection model for all grade is needed. To do  the fault detection, this paper uses DPCA which considers variable correlation and character of time dependency. At first, faulty case and grade transition case can be detected by using Hotelling’s T^2. After that, clustering analysis is used to divide normal process and abnormal process regardless to grades. As a result, grade transition states can be neglected. By using DPCA and clustering, fault detection is more efficient than using PCA.
저자 지유미1, 심예슬2, 이규황2, 이호경2, 이인범1
소속 1포항공과대, 2LG화학
키워드 이상진단
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