Online Predictive Monitoring and Prediction Model for a Periodic Process Through Multiway Non-Gaussian Modeling

作     者：Changkyoo Yoo Minhan Kim Sunjin Hwang Yongmin Jo Jongmin Oh 

作者机构：College of Environmental and Applied Chemistry Green Energy Center Kyung Hee University Gyeonggi-Do 446-701 Korea 

出 版 物：《Chinese Journal of Chemical Engineering》 (中国化学工程学报（英文版）)

年 卷 期：2008年第16卷第1期

页      面：48-51页

核心收录：

学科分类：081704[工学-应用化学] 08[工学] 0817[工学-化学工程与技术] 081701[工学-化学工程] 

基　　金：the Korea Research Foundation Grant Funded by the Korean Government (MOEHRD) (KRF-2007-331-D00089) Funded by Seoul Development Institute (CS070160) 

主　　题：inferential sensing multiway modeling non-Gaussian distribution online predictive monitoring process supervision wastewater treatment process 

摘      要：A new on-line predictive monitoring and prediction model for periodic biological processes is proposed using the multiway non-Gaussian modeling. The basic idea of this approach is to use multiway non-Gaussian modeling to extract some dominant key components from daily normal operation data in a periodic process, and subsequently combining these components with predictive statistical process monitoring techniques. The proposed predictive monitoring method has been applied to fault detection and diagnosis in the biological wastewater-treatment process, which is based on strong diurnal characteristics. The results show the power and advantages of the proposed predictive monitoring of a continuous process using the multiway predictive monitoring concept, which is thus able to give very useful conceptual results for a daily monitoring process and also enables a more rapid detection of the process fault than other traditional monitoring methods.