| 학회 | 한국화학공학회 |
| 학술대회 | 2004년 가을 (10/29 ~ 10/30, 호서대학교(아산캠퍼스)) |
| 권호 | 10권 2호, p.1225 |
| 발표분야 | 공정시스템 |
| 제목 | A novel approach for adaptive data-driven modeling |
| 초록 | Process monitoring based on PLS models has played an important role in detecting process upsets, off-spec qualities, or other special events. However, the frequent changes of operating conditions require frequent updates of models. The key of adaptive modeling is the fast and correct identification of operating mode changes from normal variations due to disturbances. This paper proposes a novel adaptive PLS modeling approach based on a method for detecting and classifying process state changes into operating mode changes or variations due to disturbances. Key idea is to extract process knowledge on detecting operating mode changes as if-then rules. If the changing state is not accepted through the defined rules, the change is classified as the variation due to disturbances. Then, PLS model is updated with two alternative update styles when identifying the changed states as mode change: update of only scaling parameter or all the model parameter. The proposed approach was applied to process data collected from an industrial fired heater. It showed less update frequency and better prediction performance than block-wise recursive PLS approach. |
| 저자 | 이영학 1 , 진형대 2 , 한종훈 1 |
| 소속 | 1 서울대, 2 포항공과대 |
| 키워드 | MSPC ; Adaptive modeling ; PLS |
| VOD | VOD 보기 |
| 원문파일 | 초록 보기 |
